WO2018205301A1

WO2018205301A1 - Mobile computing offload cooperative control system and method

Info

Publication number: WO2018205301A1
Application number: PCT/CN2017/085207
Authority: WO
Inventors: 靳浩; 王梦圆; 严士东; 赵成林; 梁栋; 彭木根; 王文博
Original assignee: 北京邮电大学
Priority date: 2017-05-08
Filing date: 2017-05-19
Publication date: 2018-11-15

Abstract

Provided are a mobile computing offload cooperative control system and method. The system comprises at least two types of pre-set controllers and a virtual controller cluster generation submodule, with the pre-set controller being one of the following: a first controller, a plurality of second controllers, a plurality of third controllers, a plurality of fourth controllers, a plurality of node level controllers, and a virtual controller cluster generation module, wherein the pre-set controllers included in different combinations of virtual controller clusters are different, and wherein the types of the first to fourth controllers may be any one of a global controller, a macro base station level controller, a micro base station level controller, and a micro cloud cluster head level controller. The present invention can complete software-defined computing offload optimization control, and can flexibly support different mobile computing offloading cooperative optimization targets centered on a user or centered on computing resource and/or network resource optimization, thereby improving the extendibility and flexibility of the mobile computing offload cooperative control method.

Description

Mobile computing offload collaborative control system and method

Cross-reference to related applications

This application claims the invention titled "Mobile Computing Unloading Collaborative Control System and Method" submitted by Beijing University of Posts and Telecommunications on May 8, 2017. The Chinese patent application number is "201710316790.8".

This application claims the priority of the invention titled "Mobile Computing Unloading Collaborative Control System and Method" submitted by Beijing University of Posts and Telecommunications on May 15, 2017. The Chinese patent application number is "201710340027.9".

Technical field

The present invention relates to the field of network communication technologies, and in particular, to a mobile computing offload collaborative control system and method.

Background technique

Recently, the number of smart mobile devices has increased significantly. At the same time, these devices have also greatly improved in terms of CPU processing power, network connection capability, sensing capability, etc., which makes people use mobile smart devices to access the network more. And to obtain related application services, mobile cloud computing (MCC: Mobile Cloud Computing) as a mobile application solution has gradually become the mainstream choice for mobile intelligent devices to obtain application services. However, in the face of implementation of computationally intensive applications such as rich media and data analysis, mobile smart devices still have serious limitations in terms of computing power and battery life. To this end, in order to improve the real-time computing efficiency of mobile cloud computing and reduce the offloading traffic burden of the WAN, Satyanarayanan proposed the concept of cloudlet, which is a trusted computer with rich computing resources located near mobile users. The terminal device can complete the edge computing task by means of the micro cloud. With the standardization of 5G technology, the edge computing-based networking method in wireless access networks will also become an important networking method, providing edge-based computing offloading through a converged network architecture based on D2D, WiFi and cellular hybrid networking. And networking will become a major development trend of the future mobile Internet, which will bring a richer QoE experience to mobile user applications, especially in cloud computing applications in real-time games, online identification, travel, and emergency scenarios.

The key technologies based on edge computing offloading mainly include computational offloading network architecture and optimization of mobile computing offloading. The computing offloaded network architecture mainly includes a computing offloading architecture based on a cellular mobile radio access network, a hybrid network architecture based on cellular and ad hoc organizations, and a computing offloading architecture based on an ad hoc network. In general, in a cellular offloading architecture based on a cellular mobile radio access network, a base station typically has computing power to support mobile computing offload services from user nodes; in a cellular and self-organizing based hybrid network architecture, base stations and users Nodes may all have computing power and can support computational offload services from users; in ad hoc-based network architectures, usually multiple user sessions Points form a micro cloud. Within a micro cloud, a user node (also called a mobile node) can accept computing offload services from other nodes in the micro cloud, or provide computing offload services to other user nodes in the micro cloud. From the optimization of mobile computing offloading, most optimizations focus on optimizing the system energy consumption. For example, a typical solution in the cellular mobile radio access network architecture is in the base station-based mobile access network. The node offloads its computing application to the base station. The optimization goal is generally to minimize the energy consumption of the system or the response time of the user application; another typical solution is that in the self-organizing network architecture, multiple user nodes form a micro cloud. In the micro cloud, user nodes can support computational offloading based on multi-hop (usually up to two hops). The optimization goal is usually to minimize the response time of application offloading; in a hybrid network architecture based on cellular and ad hoc networks, A typical solution is based on a greedy algorithm that allows each user node to offload computing applications to surrounding nodes and base stations with the goal of minimizing system power consumption.

Through the investigation of the solution based on edge computing offloading, we found that the existing mobile computing offload optimization strategy is designed according to a single determined optimization goal, and it is difficult to adapt to multiple computing offload optimization centered on user-centered or network optimization. Demand and control scalability and flexibility requirements.

Summary of the invention

The present invention aims to solve at least one of the technical problems in the related art to some extent.

To this end, an object of the present invention is to provide a mobile computing offload cooperative control system, based on a software defined perspective, using a virtual controller cluster based control manner to complete user-centric or based on computing resources and/or network resources optimization. The central computing offload cooperative control meets the scalability and flexibility control requirements of the mobile computing offload collaborative control method.

Another object of the present invention is to provide a mobile computing offload cooperative control method.

Another object of the present invention is to provide a mobile computing offload cooperative control apparatus.

Another object of the present invention is to provide a non-transitory computer readable storage medium.

Another object of the present invention is to provide a computer program product.

To achieve the above objective, the mobile computing offload cooperative control system according to the first aspect of the present invention, the mobile computing offload cooperative control system includes at least two preset controllers and virtual controller cluster generation submodules, wherein The preset controller is one of the following: a first controller, configured to collect, according to the first request, the current first network resource state information and the first computing resource state information reported by the multiple second controllers, where Obtaining a current control mode from the preset configuration table, and generating a scenario according to the calculated uninstallation information identifier and the control mode in the first request, and the current first network resource state information and the first computing resource state information The first control information corresponding to the data, and the first control information is distributed to the corresponding controller, wherein the first control information includes but is not limited to: a controller identifier, a calculation of an offload control mode, and a controlled calculation Unloading information identification; multiple second controllers for Receiving first control information distributed by the first controller, and generating a peer level according to the controller identifier in the first control information, the controlled calculation offload information identifier, and the calculation offload control manner Computing a plurality of second controllers and/or a plurality of third controllers of the lower level to perform second control information for controlling the unloading, and distributing the second control information to the corresponding second controller and/or the third a controller, the third controller, configured to: according to the controller identifier in the second control information, when the second control information distributed by the second controller is received Controlling the calculated offloading information identifier and the calculating the offloading control manner to generate third control information for controlling calculation offloading of the plurality of third controllers of the same level and/or the plurality of fourth controllers of the lower level, and The third control information is distributed to the corresponding third controller and/or the fourth controller; and the plurality of fourth controllers are configured to: according to the third control information, when the third control information is received The control The device identifier, the calculated offload control mode, and the controlled calculated offload information identifier generate a fourth control for controlling the calculation offload of the plurality of fourth controllers of the same level and/or the plurality of node level controllers of the lower level And distributing the fourth control information to a corresponding fourth controller and/or node level controller; a plurality of node level controllers, configured to acquire the fourth control information according to the fourth control information And the calculated unloading information identifier corresponding to the calculated unloading information, in the collaborative computing unit corresponding to the node level controller, calculating the offload control mode according to the fourth control information to the node level controller Controlling offloading of the corresponding collaborative computing unit is performed; the virtual controller cluster generating submodule is configured to generate a combination of different virtual controller clusters based on the at least two preset controllers, and control the mobile computing Offloading the collaborative control system to switch among the different combinations of virtual controller clusters, the mobile computing offloading collaborative control system according to the first request Controlling offloading of controllers in different virtual controller cluster combinations, wherein the preset controllers included in the combination of different virtual controller clusters are different; wherein the first controller, the first The types of the second controller, the third controller, and the fourth controller may be any one of a global controller, a macro base station level controller, a micro base station level controller, and a micro cloud cluster head level controller, respectively. Kind.

The mobile computing offload cooperative control system proposed by the first aspect of the present invention generates, by the first controller, control information corresponding to the state information according to the calculated uninstallation information identifier and the control mode in the first request when receiving the first request. And distributing control information to the second controller, the third controller, the fourth controller, and the node level controller step by step, capable of completing software-defined computing offload optimization control, flexibly supporting user-centric or based The different mobile computing offloading collaborative optimization goals centered on computing resources and/or network resource optimization, improve the scalability and flexibility of the mobile computing offload collaborative control method.

To achieve the above objective, the mobile computing offload cooperative control method according to the second aspect of the present invention includes: collecting, when generating the first request, current current network resource status information reported by the plurality of second controllers and the first Calculating resource status information, acquiring a current control mode from the preset configuration table, and generating, according to the calculated uninstallation information identifier and the control mode in the first request, the current first network resource status information and the first calculation Resource status information Generating first control information corresponding to the scene data, and distributing the first control information to a corresponding controller, where the first control information includes, but is not limited to, a controller identifier, a calculation of an offload control mode, and a control Calculating the uninstallation information identifier; receiving the first control information distributed by the first controller, and according to the controller identifier in the first control information, the controlled calculation offload information identifier, and the calculating offload control manner Generating second control information for controlling computational offloading of the plurality of second controllers of the same level and/or the plurality of third controllers of the lower level, and distributing the second control information to the corresponding second controller and And a third controller; receiving second control information distributed by the second controller, according to the controller identifier in the second control information, the controlled calculation offload information identifier, and the calculating offload control manner Generating third control information for controlling calculation offloading of the plurality of third controllers of the same level and/or the fourth controller of the lower level, and the third control letter Distributing to the corresponding third controller and/or the fourth controller; receiving the third control information, according to the controller identifier in the third control information, the calculating offload control mode, and the controlled Calculating the offload information identifier to generate fourth control information for controlling the calculation offload of the plurality of fourth controllers of the same level and/or the plurality of node level controllers of the lower level, and distributing the fourth control information to the corresponding a fourth controller and/or a node level controller; acquiring, according to the fourth control information, calculation offload information corresponding to the controlled calculation offload information identifier, in a collaborative computing unit corresponding to the node level controller, Controlling, based on the calculation of the offload control mode in the fourth control information, the calculation and offloading of the collaborative computing unit corresponding to the node level controller; generating different virtual controllers based on the at least two preset controllers a combination of clusters and controlling the mobile computing offload cooperative control system to switch among combinations of the different virtual controller clusters, the mobile computing offloading collaboration The system performs control of the unloading of the controllers in the different virtual controller cluster combinations according to the first request, wherein the preset controllers included in the combination of the different virtual controller clusters are different; The types of the first controller, the second controller, the third controller, and the fourth controller may be a global controller, a macro base station level controller, a micro base station level controller, and a micro cloud. Any of the cluster head controllers.

The mobile computing offload cooperative control method according to the second aspect of the present invention, when the first request is received by the first controller, generating control information corresponding to the status information according to the calculated uninstallation information identifier and the control mode in the first request And distributing control information to the second controller, the third controller, the fourth controller, and the node level controller step by step, capable of completing software-defined mobile computing offload optimization control, flexibly supporting user-centric or Different mobile computing offloading collaborative optimization targets centered on computing resources and/or network resource optimization, and improving the scalability and flexibility of the mobile computing offload collaborative control method.

In order to achieve the above object, a mobile computing offload cooperative control device according to a third aspect of the present invention includes:

processor;

a memory for storing processor executable instructions;

Wherein the processor is configured to:

When the first request is generated, the current first network resource status information and the first computing resource status information reported by the multiple second controllers are collected, and the current control mode is obtained from the preset configuration table, and according to the first request. Computing the unloading information identifier and the control mode to generate first control information corresponding to the current first network resource state information and the first computing resource state information generating scene data, and distributing the first control information to Corresponding controller, wherein the first control information includes, but is not limited to: a controller identifier, a calculation uninstallation control mode, and a controlled calculation uninstallation information identifier;

Receiving first control information distributed by the first controller, and generating a peer level according to the controller identifier in the first control information, the controlled calculation offload information identifier, and the calculation offload control manner Computing a plurality of second controllers and/or a plurality of third controllers of the lower level to perform second control information for controlling the unloading, and distributing the second control information to the corresponding second controller and/or the third Controller

Receiving second control information distributed by the second controller, generating, according to the controller identifier in the second control information, the controlled calculation offload information identifier, and the calculating offload control manner Calculating, by the plurality of third controllers and/or the fourth controller of the lower level, the third control information that is controlled, and distributing the third control information to the corresponding third controller and/or the fourth controller;

Receiving the third control information, generating a plurality of fourth controllers of the same level according to the controller identifier, the calculated offload control mode, and the controlled calculated offload information identifier in the third control information And/or calculating, by the plurality of node level controllers of the lower level, the fourth control information for controlling, and distributing the fourth control information to the corresponding fourth controller and/or the node level controller;

Obtaining, according to the fourth control information, the calculated uninstallation information corresponding to the controlled calculation offload information identifier, in the collaborative computing unit corresponding to the node level controller, calculating according to the fourth control information The offload control mode controls the calculation and offloading of the collaborative computing unit corresponding to the node level controller;

Generating a combination of different virtual controller clusters based on the at least two preset controllers, and controlling the mobile computing offload cooperative control system to switch among combinations of the different virtual controller clusters, the mobile computing offloading The collaborative control system controls the computing offloading of the controllers in the different virtual controller cluster combinations according to the first request, wherein the preset controllers included in the combination of the different virtual controller clusters are different;

The types of the first controller, the second controller, the third controller, and the fourth controller may be a global controller, a macro base station level controller, and a micro base station level controller, respectively. And any of the micro cloud cluster head controllers.

The mobile computing offload cooperative control apparatus according to the third aspect of the present invention, when the first request is received by the first controller, generates the first network resource and the first network resource according to the calculated uninstallation information identifier and the control mode in the first request. One Computation resource status information generates control information corresponding to the scene data, and distributes the control information to the second controller, the third controller, the fourth controller, and the node level controller step by step, and can complete the mobile computing unloading based on the software definition Optimize control and flexibly support different mobile computing offloading collaborative optimization goals centered on user-centered or based on computing resources and/or network resource optimization, and improve the scalability and flexibility of the mobile computing offload collaborative control method.

In order to achieve the above object, a non-transitory computer readable computing unloading medium proposed by the fourth aspect of the present invention, when the instructions in the computing unloading medium are executed by a controller and a processor of a mobile node, cause the controller and the mobile The node is capable of performing a mobile computing offload collaborative control method, the method comprising:

When the first request is generated, the current first network resource status information and the first computing resource status information reported by the multiple second controllers are collected, and the current control mode is obtained from the preset configuration table, and according to the first request. Computing the unloading information identifier and the control mode to generate first control information corresponding to the current first network resource state information and the first computing resource state information generating scene data, and distributing the first control information to Corresponding controller, wherein the first control information includes, but is not limited to: a controller identifier, a calculation uninstallation control mode, and the controlled calculation uninstallation information identifier;

Obtaining, according to the fourth control information, the calculated uninstallation information corresponding to the controlled calculation offload information identifier, in the collaborative computing unit corresponding to the node level controller, calculating according to the fourth control information The unloading control mode controls the calculation and unloading of the collaborative computing unit corresponding to the node level controller;

Generating a combination of different virtual controller clusters based on the at least two preset controllers, and controlling the mobile computing offload cooperative control system to switch among combinations of the different virtual controller clusters, the mobile computing offloading The collaborative control system controls the computational offloading of the controllers in the different virtual controller cluster combinations according to the first request, where The preset controllers included in the combination of the different virtual controller clusters are different;

The non-transitory computer readable computing unloading medium proposed by the embodiment of the fourth aspect of the present invention, when the first request is received by the first controller, according to the calculation of the uninstallation information identifier and the control mode generated in the first request, and the network resource and Computation resource status information generates control information corresponding to the scene data, and distributes the control information to the second controller, the third controller, the fourth controller, and the node level controller step by step, and can complete the mobile computing unloading based on the software definition Optimize control and flexibly support different mobile computing offloading collaborative optimization goals centered on user-centered or based on computing resources and/or network resource optimization, and improve the scalability and flexibility of the mobile computing offload collaborative control method.

In order to achieve the above object, a computer program product according to an embodiment of the fifth aspect of the present invention, when an instruction in the computer program product is executed by a processor, performs a mobile computing offload cooperative control method, the method comprising:

Obtaining the calculated uninstallation information corresponding to the controlled calculation uninstallation information identifier according to the fourth control information, where a collaborative computing unit corresponding to the node level controller, configured to control, according to the calculating an unloading control manner in the fourth control information, a calculation and uninstallation of a collaborative computing unit corresponding to the node level controller;

According to the computer program product of the fifth aspect of the present invention, when the first request is received by the first controller, the control information corresponding to the status information is generated according to the calculated uninstallation information identifier and the control mode in the first request, and The control information is distributed to the second controller, the third controller, the fourth controller, and the node level controller step by step, and can complete the software-defined mobile computing offload optimization control, and flexibly support user-centered or computing-based resources. And/or network resource optimization centered on different mobile computing offloading collaborative optimization goals, improving the scalability and flexibility of the mobile computing offload collaborative control method.

The additional aspects and advantages of the invention will be set forth in part in the description which follows.

DRAWINGS

The above and/or additional aspects and advantages of the present invention will become apparent and readily understood from

1 is a schematic structural diagram of a mobile computing offload cooperative control system according to an embodiment of the present invention;

2 is a schematic structural diagram of a mobile computing offload cooperative control system according to another embodiment of the present invention;

3 is a schematic structural diagram of a first network resource status statistics sub-module according to an embodiment of the present invention;

4 is a schematic structural diagram of a first computing resource status statistics sub-module according to an embodiment of the present invention;

5 is a schematic structural diagram of a first service proxy submodule in an embodiment of the present invention;

6 is a schematic structural diagram of a first user mobile computing offload information analysis sub-module according to an embodiment of the present invention;

7 is a schematic structural diagram of a first control information generating submodule in an embodiment of the present invention;

FIG. 8 is a schematic structural diagram of a first controller control submodule according to an embodiment of the present invention; FIG.

9 is a schematic diagram of a general function structure of a mobile computing offload cooperative controller in an embodiment of the present invention;

FIG. 10 is a diagram showing functions of various levels of mobile computing offload cooperative controller based on centralized control mode in an embodiment of the present invention; Schematic diagram of sub-module;

11 is a schematic diagram of a functional sub-module of a mobile computing unloading cooperative controller based on a hybrid control mode according to an embodiment of the present invention;

12 is a schematic diagram of a functional sub-module of a mobile computing unloading cooperative controller at all levels based on a fully distributed control mode according to an embodiment of the present invention;

FIG. 13 is a schematic diagram of a workflow of optimizing a number of micro base station level cooperative controllers based on a macro base station level cooperative controller according to an embodiment of the present invention; FIG.

FIG. 14 is a schematic diagram of a workflow for optimizing a number of micro cloud cluster head level controllers based on a micro base station level controller according to an embodiment of the present invention; FIG.

15 is a schematic diagram showing the general function structure of a node-level mobile computing offload cooperative controller in an embodiment of the present invention;

16 is a schematic flowchart of a mobile computing offload cooperative control method according to an embodiment of the present invention;

FIG. 17 is a schematic flowchart of a mobile computing offload cooperative control method according to another embodiment of the present invention.

detailed description

The embodiments of the present invention are described in detail below, and the examples of the embodiments are illustrated in the drawings, wherein the same or similar reference numerals are used to refer to the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are intended to be illustrative of the invention and are not to be construed as limiting. Rather, the invention is to cover all modifications, modifications and equivalents within the spirit and scope of the appended claims.

Before describing the present invention, it can be seen that the mobile computing offload cooperative control system includes a control plane composed of a plurality of coordinated controllers, and a data plane composed of mobile radio access network resources and computing resources, wherein the data flow of the control plane is used for The control information interaction between the controllers and the general nodes in the control system, and the control information exchange between the mobile computing and the unloading cooperative controllers. The data plane is mainly responsible for application call data based on computational unloading between the collaborative computing units to which each controller (referred to as a computing offload node on the data plane), between the collaborative computing unit and the general node, and between the general node and the general node. Interactions, interactive data streams transmitted over the core network and the mobile radio access network include call redirection data based on computational offloading, data that invokes input values and calls return values, and the like.

FIG. 1 is a schematic structural diagram of a mobile computing offload cooperative control system according to an embodiment of the present invention.

Referring to FIG. 1 , the mobile computing offload cooperative control system includes at least two preset controllers and a virtual controller cluster generation submodule 600, wherein the preset controller is one of the following: the first controller 100, multiple The second controller 200, the plurality of third controllers 300, the plurality of fourth controllers 400, and the plurality of node level controllers 500.

In the embodiment of the present invention, the types of the first controller 100, the second controller 200, the third controller 300, and the fourth controller 400 may be global controller, macro base station level controller, and micro base station level control, respectively. And micro cloud cluster head Any of the level controllers. The first controller 100, the second controller 200, the third controller 300, and the fourth controller 400 may be named as corresponding levels of mobile computing offload cooperative controllers.

For example, the mobile computing offload cooperative control system may include a data center (IDC) of a core network, a macro base station, a micro base station, a wireless access point, a radio access control point, and a micro cloud cluster head, wherein the IDC, the macro base station, and the micro base station The wireless access point, the wireless access control point, and the micro cloud cluster head can all be configured with a mobile computing offload cooperative controller to provide a mobile computing offload cooperative control function, and each mobile computing offload cooperative controller and a general mobile node can selectively A collaborative computing unit is configured to provide computing resources and their computing offload services. The mobile computing offloading cooperative controller may also have no cooperative computing unit, that is, only the control function of the mobile computing offloading cooperative controller is supported. Generally, mobile nodes can form a mobile micro cloud. The cluster head of the micro cloud is usually a mobile PC, a tablet computer or a smart terminal device with rich computing resources. The micro cloud cluster head supports the function of the mobile computing offloading collaborative controller, and completes the pair. Computational offload cooperative control of each mobile computing node with collaborative computing functions within the micro cloud. Generally, the mobile node can choose whether to support the micro cloud collaborative computing offload function based on the self-organizing network.

It is worth noting that the selection of the micro cloud cluster head can be completed by the micro base station, the wireless access point, the wireless access control point or the macro base station controller to which the micro cloud node belongs, and the current endurance capability and computing power of the node in the micro cloud can be As the basis for its choice as a micro cloud cluster head. If a node requests to calculate the offload service, neighboring nodes in the micro cloud can support computational offloading, then the node can offload the computation to the neighboring node without having to go to the farther base station and The macro base station performs computational offloading, which can reduce the response delay of the user to obtain the computing offload service, and at the same time, fully utilize the computing resources of the edge device.

The mobile computing offloading collaborative controller is directed to a mobile computing offloading service request from a user in the control system and a computing offload control service service request from the system's network resources and/or computing resource optimization, based on system performance metrics and/or user quality of service experience The evaluation indicators, as well as the network resources and/or computing resource state analysis functions, mobile computing offload optimization functions, and optimization result information distribution functions configured on the controller and the mobile node in the system, optimize the control offloading in the system.

In the control system, the collaborative unloading controller can be controlled by a single mobile computing to control the system's computing offload optimization process, or multiple controllers can be clustered by a virtual controller cluster formed based on a certain controller. Control the mobile computing offload optimization process. The mobile computing offloading cooperative controller based on the micro cloud cluster head can directly control a single mobile computing node (that is, it can also be a mobile computing node at the same time), and can also control multiple mobile computing nodes of the entire micro cloud.

It is worth noting that mobile users (general nodes) can choose to support self-organizing-based networking and computing offload services, so that users can obtain computing offload services based on micro-clouds of self-organizing networking. The mobile computing offload cooperative controller can be placed at the core network, at the macro base station of the access network, at the micro base station, at the wireless access point, the wireless control point, and the micro cloud cluster At the head, the computational offload cooperative control between the collaborative controllers is uninstalled by mobile computing, and the mobile computing offload collaborative optimization control based on specific optimization objectives is completed.

In an embodiment of the present invention, the mobile computing offload control system includes: a first controller 100, configured to collect current first network resource status information and a number reported by the plurality of second controllers when the first request is generated And calculating a resource state information, acquiring a current control mode from the preset configuration table, and generating, according to the calculation of the uninstallation information identifier and the control mode in the first request, the current first network resource state information and the first computing resource state information generation scenario. The first control information corresponding to the data, and the first control information is distributed to the corresponding controller, wherein the first control information includes, but is not limited to, a controller identifier, a calculation uninstallation control mode, and a controlled calculation uninstallation information identifier.

In the embodiment of the present invention, the preset configuration table may be configured in advance, and the control mode of the controller is stored in the preset configuration table, where the control mode includes: a first type control mode and a second type control mode, wherein One type of control mode is the same control mode as the controller topology and the physical calculation of the unloading node topology of the mobile radio access network, and the second type of control mode is different for the controller topology and the physical calculation of the unloading node topology of the mobile radio access network. Control mode.

When the control topology of the controller is consistent with the network topology of the physical computing offload node where the controller is located, that is, the radio access network of the controller is physically calculated and the node is unloaded due to the hierarchical structure of the cellular network, so that the control architecture between the controllers is When the hierarchical structure of the physical computing offload node overlaps, the mobile computing offload cooperative controller performs mobile computing offload cooperative control based on the hierarchical structure. Taking the first controller 100 as a global controller as an example, the mobile computing offload cooperative control system can be divided into two layers, and the first layer is a global control layer, that is, the first controller 100 is placed in the first layer and becomes a global controller. Located at the data center outside the MNO, it is responsible for providing global optimization control of the mobile computing offload service. When the first request is generated, the current first network resource state information and the first computing resource state information reported by the plurality of macro base station level controllers (ie, the second controller 200) are collected, and the current control is obtained from the preset configuration table. And generating, according to the calculated uninstallation information identifier and the control mode in the first request, first control information corresponding to the current first network resource state information and the first computing resource state information generation scenario data, and distributing the first control information To the corresponding controller; the second layer may also be a mobile radio access network based on heterogeneous convergence, and the mobile computing offload cooperative controller may be located at the radio access control point and the macro base station, the above-mentioned based on the macro base station level, based on the wireless connection The mobile computing offloading cooperative controller entering the control point receives the first control information distributed by the first controller 100.

In some embodiments, referring to FIG. 2, the first controller 100 includes: a first network resource status statistics sub-module 110, a first computing resource status statistics sub-module 120, a first service agent sub-module 130, and a first user mobile computing unloading. The information analysis sub-module 140, the first control information generation sub-module 150, and the first distribution sub-module 160. among them,

The first network resource status statistics sub-module 110 is configured to collect a plurality of second controllers 200, and/or a plurality of third controllers 300, and/or current network resource status information of the network to which the plurality of fourth controllers 400 belong. As the first network resource status information.

The network resource status information includes node resource status information and link resource status information, and the node resource status information includes but is not limited to information such as power consumption of the node.

Taking the first controller as a global controller as an example, receiving network resource status information from each macro base station controller, and/or micro base station level controller, and/or micro cloud cluster head level controller, and performing global based The network resource status information is collected and analyzed, and the generated scene data is used as an input of the first control information generation sub-module 150, and is used to implement a global perspective calculation and offload collaborative optimization control strategy.

As shown in FIG. 3, the first network resource status statistics sub-module 110 includes a network resource status information collection unit 111, a pre-processing unit 112, a data analysis unit 113, a prediction unit 114, and an information aggregation unit 115. Specifically, the network resource status information collecting unit 111 periodically periodicizes the network resource status information to which the plurality of second controllers 200, and/or the plurality of third controllers 300, and/or the plurality of fourth controllers 400 belong. The information is collected, and the part of the network resource status information is input to the pre-processing unit 112 for pre-processing, and the information after the pre-processing is input to the data analysis unit 113 for processing, and the output is output from the prediction unit 114 and the information aggregation unit 115. The network resource state prediction information and the information aggregation result of the network resource state information generate and output scene data based on the network resource state information according to the prediction information and the information aggregation information.

The first computing resource status statistics sub-module 120 is configured to collect a plurality of second controllers 200, and/or a plurality of third controllers 300, and/or current computing resources of the coordinated computing units to which the plurality of fourth controllers 400 belong The status information is used as the first computing resource status information.

The computing resource status information includes, but is not limited to, a computing capability of the node, a current usage rate of the computing resource controlled by the node, and an actual computing resource usage rate based on a specific computing resource configuration manner.

Taking the first controller 100 as a global controller as an example, the first computing resource state statistics sub-module 120 of the controller receives the macro base station controller, and/or the micro base station level controller, and/or the micro cloud cluster head. The level controller calculates the resource state information, and performs statistics and analysis based on the global computing resource state information, and the generated scene data is used to implement the global off-view computing offload collaborative optimization control.

As shown in FIG. 4, the first computing resource state statistics sub-module 120 can be used to collect and analyze the computing resource state information to which the first controller 100 belongs. The module 120 includes: a computing resource state information collecting unit 121, a pre-processing unit 122, The data analysis unit 123, the prediction unit 124, and the information aggregation unit 125. The computing resource status information collecting unit 121 collects the coordinated computing to which the plurality of second controllers 200, and/or the plurality of third controllers 300, and/or the plurality of fourth controllers 400 belong to the computing resource control interface module. The computing resource status information of the unit is input to the pre-processing unit 122 for pre-processing, and the output computing resource status information is input to the data analyzing unit 123 for analysis, and the output information is input to the prediction unit 124 and the information. Aggregation unit 125, based on prediction unit And the information aggregation unit generates and outputs scene data based on the computing resource status information. Since the collaborative computing unit is an optional configuration device, the computing resource state information of the collaborative computing unit is selectable, for example, obtaining the computing resource usage rate of the currently associated collaborative computing unit, calculating the uninstalled popular application/component, and the user's mobile The change feature of the uninstall service request is calculated and used as the optimal control basis of the first control information generating sub-module 150.

The first service proxy sub-module 130 is configured to receive a user request, and determine, according to the user request, whether the first network resource state information and the first computing resource state information meet the preset condition, and generate the first request when the preset condition is met. The first request includes, but is not limited to, a computing offload information identifier corresponding to the user request.

In an embodiment of the present invention, the preset condition is preset by the built-in program of the unloading cooperative controller, and is used to determine whether to generate a user computing uninstall service request and a computing offload control service based on network resources and/or computing resource optimization. Request for service.

Optionally, the first service proxy sub-module 130 receives the user request, and receives the scenario data generated by the first network resource state statistics sub-module 110 based on the first network resource state information, and the first computing resource state statistics sub-module 120 is based on the first calculation. The scenario data generated by the resource state information is analyzed, and the scenario data is analyzed to determine whether the preset condition is met, that is, whether to generate a computing offload control service service request based on network resources and/or computing resource optimization, when the determination result is not When the computing offload control service service request based on the network resource and/or the computing resource optimization is generated, the scenario data of the first network resource state statistics sub-module 110 and the scenario data of the first computing resource state statistics sub-module 120 are continuously received, and received. The scenario data is analyzed, and when the result of the determination is to generate a computing offload control service service request based on network resources and/or computing resource optimization, a first request is generated.

As an example, referring to FIG. 5, the function sub-module included in the first service proxy sub-module 130 has a computing resource and network resource optimization-centered computing offload control service service request generating unit 131, a computing offloading service request queue unit 132, and a calculation. The service request scheduling unit 133 is uninstalled.

The computing resource and network resource optimization-centered computing offload control service service request generating unit 131 receives the scenario data from the first network resource state statistics sub-module 110 and the scenario data of the first computing resource state statistics sub-module 120, and It analyzes whether to generate a computing offload control service service request based on network resource and/or computing resource optimization; when the decision generates a computing offload control service service request based on network resources and/or computing resource optimization, the request is input to Calculating the unloading service request queue unit 132; when the decision does not generate a computing offload control service service request based on the network resource and/or the computing resource optimization, continuing to receive the status from the first network resource state information statistics sub-module 110 and the first computing resource The scene data of the information statistics sub-module 120 is analyzed and analyzed. The calculation offload service request queue unit 132 receives the network offload control service service request based on the network resource and/or the computing resource optimization and the mobile computing offload service request from the user, and based on the scheduling rule, the offload service request scheduling unit 133 calculates the network based Resource and/or computing resource optimized computing offload control business service request and user movement The dynamic computing offload service request provides a scheduling service. For example, the scheduling of the mobile computing offload service request of the user may be completed in real time, and the optimization control of the computing offload control service service request based on the network resource and/or the computing resource optimization is completed when the network is not peak.

In the embodiment of the present invention, the calculation of the uninstall service request may be used by the user to obtain a request for calculating the uninstall service in the network, where the user request includes, but is not limited to, the user node identifier, the calculation of the uninstallation information identifier, and the calculation of the uninstall service request. It may be a generated computing offload control service service request based on network resources and/or computing resource optimization. For example, when the service request is a mobile computing offload service request from the user, according to the functional module of the above-mentioned user, that is, the general node, the general node supporting the mobile computing offload function collects the node through its resource state information statistics and analysis submodule. The resource status information is analyzed, and the current resource status information of the current node and the resource status information of the application load and the remaining energy of the node are analyzed, and the local execution time and the uninstall execution time of each application that the current node needs to execute are given. The predicted evaluation result is combined with the related networking information of the current local node of the node management module. When the evaluation result performed for an application does not meet the current specified optimization target of the node, for example, but not limited to, the following conditions are met: The local execution time is greater than the calculation of the offload execution time, the power consumption of an application when it is executed locally is too large, the life time of the node is difficult to reach the maximum, the energy efficiency of the node is optimal, and the local computing resources are difficult to provide the required computing resources. Node unloading optimization The policy sub-module will issue a user mobile computing offloading service request for the application to the service proxy sub-module, and the service proxy sub-module sends out to the mobile computing unloading cooperative controller to which it belongs by using the interface sub-module of the mobile computing offloading cooperative controller. The user mobile computing offloads the service request; otherwise the application will execute locally at the node and the node will not issue a user mobile computing offload service request for the application. When the user issues a mobile computing offloading service request to the mobile computing offloading controller of the area in which it is located, the mobile computing offloading cooperative controller receives the mobile computing offloading service request from the user, and extracts the calculated offloading service information required in the service request Including, but not limited to, computing application information that needs to be uninstalled, user computing uninstallation information identifier, determining whether the computing application service is supported in the component registry of the area controlled by the controller, and if the computing application service is supported, the controller is based on Collaborative control of other controllers and their computing resources controlled by them, collecting state information of network resources and computing resources, converting the service request into a computational unloading optimization problem based on a specific optimization target, and generating control information through the controller The sub-module gives a calculation of the unloading optimization result, and the controller performs the calculation of the unloading optimization result by the controller and/or the computing resource to which the controller belongs. At the same time, the controller notifies the user to obtain the manner of calculating the unloading service; Unloading optimization knot based on this calculation If the information is related to the operation, the application computing interaction, the calculation application instantiation, and the like; if the computing application service belongs to the control area to which the controller belongs, the controller determines whether the upload of the mobile computing uninstall service request is supported, if Supporting the uploading of the service request, the controller uploads the unloading service request that cannot be satisfied to the upper-level computing unloading cooperative controller, and the upper-level computing unloading cooperative controller starts to process the computing unloading service request, if This controller does not support computing unloading service requests. The uploading, the controller exchanges information with other controllers, and collects state information of related network resources and computing resources based on the computing resources and computing application service information controlled by the controller, and performs an offloading service request based on the mobile computing. Calculating the unloading collaborative optimization control process, giving the calculation of the unloading optimization result, and distributing the calculation unloading optimization result to the controller and/or the computing resource of the control by the controller unloading the coordinated control strategy distribution sub-module, and simultaneously, the controller notifies The user obtains a method for calculating the uninstall service, and the associated computing resource performs an operation of invoking the optimization result based on the calculation, and performing operations such as calling the computing application and calculating the application. When the service request is a computing offload control service service request optimized based on network resources and/or computing resources, the statistics unloading collaborative controller has statistical and analysis functions of network resources and computing resource state information, and thus can generate The scenario data of the current network resource and the computing resource state, the service agent sub-module may decide whether to generate a computing offload control service service request based on the network resource and/or the computing resource optimization according to the scenario data. If the service agent sub-module of the controller issues a computational offload control service service request based on network resources and/or computational resource optimization, the controller is responsible for performing a related computational offload optimization process. The global controller, the macro base station level controller, the micro base station level controller, and the micro cloud cluster head level controller may all issue a computing offload control service service request based on network resources and/or computing resource optimization of the area controlled by the controller. And complete the computational offload optimization control centered on the mobile computing offloading collaborative controller.

The first user mobile computing uninstallation information analysis sub-module 140 is configured to acquire, according to the calculated uninstallation information identifier in the first request, the calculated uninstallation information corresponding to the calculated uninstallation information identifier, based on calculating the unloaded historical data, and sending the node requested by the user. The history information generates the prediction information related to the calculation offload.

Optionally, the first user mobile computing offload information analysis sub-module 140 obtains the computing application uninstallation information corresponding to the calculated uninstallation information identifier according to the calculated uninstallation information identifier in the first request, and then, based on the calculation of the uninstalled historical data and the sending The history information of the user node requested by the user generates prediction information based on the user and its node device, for example, statistics of computing application service information of different macro cells to which the user belongs, analysis of request degree change of the application, and change of computing application service demand of different types of users In order to serve as a basis for providing regional computing applications and optimizing computing offload strategies.

Due to the energy consumption of the system, the user mobile computing offload service information analysis sub-module is an optional function sub-module in the third-level controller, the fourth-level controller, and the node-level controller.

As shown in FIG. 6, the first user mobile computing uninstallation information analysis sub-module 140 is configured to collect and analyze the user computing uninstall service information to which the first controller 100 belongs, including: the user computing uninstall service information collecting unit 141, and the pre-processing unit 142. The data analysis unit 143 based on the historical data and the user mobile calculation offload service information prediction unit 144. Specifically, the user calculation offloading service information collecting unit 141 collects the associated user computing offloading service information from the first controller 100, and inputs the present information to the preprocessing unit 142 for preprocessing, and the outputted information is input to the historical data based data. The analysis is performed in the data analysis unit 143, and the analysis result information is input to the user mobile calculation offload service information prediction unit 144, and the pre-processing service related prediction unit 144 is generated and outputted from the user mobile calculation offload information service prediction unit 144. The measured data is used as an optimization basis for the mobile computing offload cooperative optimization control strategy sub-module (ie, the first control information generating sub-module 150).

The first control information generating sub-module 150 is configured to generate, according to the calculated uninstallation information identifier and the preset configuration table in the first request, a first corresponding to the current first network resource state information and the first computing resource state information generated scene data. Control information, wherein the first control information is used to control the calculated offload of the controller of the controller that is in the network to which the controlled computing offload corresponds to the controller identifier and/or the controller information to which the predictive information is directed.

Specifically, the first control information generating sub-module 150 calculates an offloading service request and a network resource and/or a computing resource according to the calculated uninstallation information identifier, the preset configuration table, and the user mobility input by the first service proxy sub-module 130 in the first request. The optimized computing offload control service service request, the first network resource state statistics sub-module 110 is based on the scenario data generated by the first network resource state information, and the scenario data generated by the first computing resource state statistics sub-module 120 based on the first computing resource state information. And generating, based on the prediction information generated by the first user mobile computing offload information analysis sub-module 140, a computational offload corresponding to the computing offload request service from the user and the computing offload control service service request from the system for network resource and/or computing resource optimization. Collaboratively optimizing the control result, and performing optimization control on calculating the unloading of the controller marked by the controller identifier in the network corresponding to the controller identifier and/or the prediction information.

As an example, the first control information generation sub-module 150 includes: a collaborative computing offload control system optimization target function conversion unit 151, configured to uninstall the information identifier and the preset configuration table according to the calculation in the first request, and the first network resource. The state information and the first computing resource state information generate scene data, and convert the first request into a specific target-based computing offload optimization problem; the algorithm selection decision unit 152 is configured to perform an optimization algorithm selection decision according to the calculation offload optimization problem; the algorithm unit 153 And the algorithm for calculating the unloading optimization result obtained by the optimization algorithm selected according to the decision, and generating the first control information.

Specifically, as shown in FIG. 7, the collaborative computing offload control system optimization objective function conversion unit 151 receives the user mobile computing offload service request from the first service proxy submodule 130 or the network resource and/or computing resource optimization calculation from the system. Unloading the control service service request, and calculating the scenario data output by the resource state statistics sub-module and the network resource state statistics sub-module, converting the scenario data information and the service request information into a mobile computing offload optimization problem based on a specific optimization target, and outputting to The algorithm selection decision unit 152 selects an algorithm for the problem according to the type of the optimization problem, that is, selects an online algorithm module or an offline algorithm module to obtain an optimal control result of the optimization problem. The algorithm unit 153 includes an online algorithm unit and an offline algorithm unit, wherein the online algorithm unit includes a mapping rule set subunit and a rule performance evaluation subunit, and the mapping rule set subunit is used to provide a common mapping rule applicable to the online algorithm, and the rule performance The evaluation subunit is configured to evaluate the optimization result of the algorithm according to the performance index; the offline algorithm unit includes a simulation model subunit and a rule adaptation subunit, and the simulation model subunit is used to store a common mobile computing offload control system optimization target and its corresponding Optimize simulation result data, For offline algorithm reading, the rule adaptive sub-unit is used to dynamically match the optimal control mode corresponding to the scene data.

a first distribution sub-module 160, configured to distribute the first control information to the controller identifier corresponding to the controller identifier and/or the controller identifier in the network pointed to by the controlled calculation offload control, so as to The marked controller controls the calculation offload based on the first control information and the offload control mode.

For example, the first controller 100 is a global controller, and the first distribution submodule receives the calculation offload cooperative optimization control result from the first control information generating submodule 150, and outputs the optimized control result information to the macro base step by step. Station, micro base station, wireless access point, wireless access control point, micro cloud cluster head and/or user.

Specifically, the sub-module 160 is configured to obtain a calculation offload optimization result from the first control information generation sub-module 150, and distribute the result information. The distribution result information includes, but is not limited to, calculation offload information for a specific application, that is, uninstalled node information of an application that needs to be uninstalled, uninstall node information, and a call relationship between applications and the like. For example, if a computational offload is performed based on a component, the submodule 160 is configured to distribute the component based optimal offload deployment result; when the component manager needs to perform a remote call, the submodule 160 is rendered according to the first control information generation submodule 150. The component optimizes the unloading deployment result, instantiates the components required for the service request on the corresponding target collaborative computing unit, and returns the address and/or identity of the target collaborative computing unit to the offloading node requesting the mobile computing offloading service request to propose A component-based offload-based association is established between the offload node of the service request and the target collaborative computing unit to make a component call.

As an example, the following is a mobile computing offload cooperative control system based on computational resource and/or network resource optimization-centered computing offload collaborative optimization control angle and user-centered mobile computing resource offload optimization control. Working mechanism.

From the perspective of computing offloading collaborative optimization control centered on computing resources and/or network resource optimization, first, the first network resource state statistics sub-module 110 and the first computing resource statistics sub-module 120 perform network resource information and computing resource information. Pre-processing, wherein the pre-processed computing resource status information is input to the first user mobile computing unloading information analysis sub-module 140 for analysis, and the analysis result is output to the information convergence sub-module, and at the same time, the analysis result and the output of the information convergence sub-module The scene data of the computing resource state of the first user mobile computing unloading information analysis sub-module 140 is formed; the pre-processed network resource state information is input to the first network resource state statistics sub-module 110 and the first computing resource statistics sub-module 120. Analysis, the analysis result is output to the information convergence sub-module, and the analysis result and the output of the information convergence sub-module form scene data based on the network resource status information. The generated scenario data of the current computing resource and the network resource state is output to the first service proxy sub-module 130, and the computing resource and network resource optimization centered in the first service proxy sub-module 130 is a central computing offload control service service request generating unit. Determining, according to the scenario data, whether to generate a computing offload control service service request based on network resources and/or computing resource optimization, and if generating a computing offload control service service request based on network resources and/or computing resource optimization, the request is input to First When the service request scheduling unit 133 processes the service request, the scenario data corresponding to the request is input to the first control information generation sub-module 140 along with the processing of the service request. And for completing a computing resource offload optimization control policy corresponding to the service request.

From the perspective of user-centric mobile computing resource offload optimization control, the mobile computing offload service request issued by the mobile user is input to the computing offload service request queue unit 132 of the first service proxy sub-module 130. The service request scheduling unit 133 of the first service proxy sub-module inputs the request to the first user mobile computing uninstallation information analysis sub-module 140 for processing according to the requested scheduling rule, and the first user mobile computing uninstallation information analysis sub-module 140 The pre-processing unit 142 extracts information related to the mobile computing offload control in the request and inputs it to the data analysis unit 143 based on the history data, and the data analysis unit 143 based on the history data performs the history data according to the input related information. The analysis provides the prediction information related to the calculation and offloading of the service request, and the related information of the service request is processed by the information aggregation unit 144, and the scene data corresponding to the service request information is output, and the scene data is generated as the first control information. The input data of the sub-module 150 is used to complete the mobile computing offload cooperative optimization control corresponding to the user mobile computing offload service request.

The first control information generating submodule 150 receives the network data from the system and/or the scenario data of the computing resource optimized computing offload control service service request and/or the scenario data from the user's mobile computing offloading service request, and completes the corresponding data according to the data. The mobile computing offload collaborative optimization control for computing the offload service request. The optimized control result for the mobile computing offloading collaborative optimization problem is obtained by an offline algorithm or an online algorithm and output to the first distribution sub-module 160, which is responsible for distributing the mobile computing offloading collaborative optimization control result information to the coordinated computing to which the corresponding controller belongs. Unit and mobile radio access network resources, the collaborative computing unit completes the user-centric or computing resource and/or network resource optimization-centered mobile computing offloading and computing application instantiation, calling, etc., calculating the unloading related data via the core The network and/or mobile radio access network transmits between the offloaded node and the offloaded node.

As a specific example, since the first controller 100 is a global controller, there is no function of interfacing with the upper controller; the submodule with the next controller mainly completes the global controller and the next subordinate thereof The control information interaction between the level controllers (ie, the macro base station controllers); the peer controller interface sub-modules complete the control information interaction between the global controllers having the same level as the controller; the resource control interface sub-modules are completed The global controller interacts with control information between network resources and computing resources it controls.

Optionally, the first controller 100 further includes: a first controller control submodule 170, a first application management submodule 180, and a first node management submodule 190. among them,

The first controller control sub-module 170 is configured to control and manage control information and interaction between the first controller and a controller associated with the first controller.

Specifically, the first controller control sub-module 170 is configured to complete control of the controller and controller cluster-based control Control function. The main functions of this module include: initialization of controller state based on different modes, monitoring of control status information of the controller, updating of the control mode of the controller and its controlled controller, storage and controller/controller Data information related to the control mode of the cluster, evaluation of the control state of the controller/controller cluster, and reliability management of the controller/controller cluster.

As shown in FIG. 8, the first controller control sub-module 170 includes: a controller operation mode control unit 171, a controller state monitoring unit 172, a controller state evaluation unit 173, a controller state information storage unit 174, and a controller. The sex management unit 175 and the controller mode control information interaction unit 176, the specific functions of each unit are as follows:

(1) controller working mode control unit 171: the unit 171 completes the initialization process of the controller state based on different modes, monitors the association state of the controller and its neighboring controllers, and interacts with the adjacent controllers to control state information according to Control state information with the adjacent controller, update the control topology map based on the controller cluster of the controller, enable the state information of the controller function module of the controller based on different controller clusters, and based on different virtual controller clusters The network resources and computing resource information that can be monitored/controlled are transmitted to the controller state information storage unit 174 for storage.

(2) The controller state monitoring unit 172: collects the operating state data of the controller, monitors the control state information of the controller and the controller cluster it controls, and periodically transmits the control state information to the controller state evaluating unit 173. Evaluation. If the unit 172 receives the abnormality evaluation result from the controller state evaluation unit 173, the abnormality evaluation information is transmitted to the reliability management unit 175 of the controller.

(3) Controller state evaluation unit 173: receives controller state information from the controller state monitoring unit 172, and controls the controller and its associated controller cluster based on the performance evaluation index of the controller and the virtual controller cluster. The evaluation is performed, if the state is normal, the periodic evaluation of the received status information data is continued; if the status is not normal, the control information is sent to the reliability management unit 175 of the controller, and the abnormal result is restored.

(4) Controller status information storage unit 174: The unit 174 mainly receives and stores control status information from the controller of the controller operation mode control unit 171, and the stored controller control status information mainly includes but is not limited to based on the present control. The controller is a cluster head and associated controller control information including each virtual controller cluster whose controller is a member of the controller cluster, including but not limited to the associated state information of the controller and other controllers, and the controller cluster. Control topology map, controller function module enable status information of the controller based on different controller clusters, network resources and computing resource information that can be monitored/monitored and controlled/controlled based on different controller clusters.

(5) Controller reliability management unit 175: This unit 175 is responsible for performing recovery control when the controller/controller cluster is in an abnormal control state. The unit 175 performs matching based on the reliability management rule, and transmits the corresponding restoration control information under the matching rule to the controller working mode control unit 171.

(6) Controller mode control information interaction unit 176: Completing the controller associated with the controller of the controller The controller controls the information interaction.

The first application management sub-module 180 is configured to store and manage a computing application supported by the first controller, and invoke the application according to the first request, where the first application management sub-module 180 includes a registry, an application manager, and a computing application. program.

Specifically, the first application management sub-module 180 includes an application registry 181 and an application manager 182 and a computing application 183.

The application registry 181 is used to store all applications supported by the computing resources to which the first controller 100 belongs. Optionally, when the first controller 100 configures the collaborative computing unit, in the component-based computing uninstallation, the application registry 181 is configured to save component information of all applications supported by the associated collaborative computing unit, and the information is The union of all the general nodes and the component registry information on the component manager on the collaborative computing unit. Through the component registry, the first controller 100 can provide all of the application component services listed in the registry to all of the general nodes and other controllers it controls.

The application manager 182 is used to manage all applications and their invocation relationships when uninstalling, and can support remote management functions. Optionally, when the first controller 100 configures the collaborative computing unit, in the component-based computing unloading, the application manager 182 is used to manage all components of an application and handle the calling relationship between the components. For example, when a user launches an application, the operating system creates a component manager for the application, and the application registers all of its components in the component manager, and also calls the relationship between the components, parameter information, return value information, and The estimated execution time is saved in the component registry, which is also synchronized to the controller's component manager; when a call between components occurs, for example, component C1 calls component C2, the caller will go to the node. The component manager makes the request, the component manager finds the requested component C2 in the registry, and forwards the call request to component C2. If the called component C2 is running locally, this is a local call; if component C2 To make a call to component C3, the unloaded optimized component deployment result is calculated according to the controller, and if component C3 is offloaded to another collaborative computing unit to which the controller belongs, component manager control component C3 is instantiated on the collaborative computing unit. The call to component C3 by component C2 will be redirected to the collaborative computing unit by the component manager.

The computing application 183 refers to a computing application that the controller can support and resides in the collaborative computing unit; when the computing unloading is based on component unloading, the application is an application that can be split based on the component.

The first node management sub-module 190 is configured to manage, by the first controller, the associated general node and/or the collaborative computing unit.

Since the function of the first node management sub-module 190 is related to the collaborative computing unit and the general node and the collaborative computing unit to which it belongs, and the number of general nodes and the configuration of the collaborative computing unit can be changed, the first node management sub-module Some of the features of the 190 are optional.

In an embodiment of the present invention, the mobile computing offload cooperative control system includes: a plurality of second controllers 200, configured to receive first control information distributed by the first controller 100, and according to a controller in the first control information Identifying, calculating an offload control mode, and controlling the calculated offload information identifier to generate second control information for controlling calculation offloading of the plurality of second level controllers 200 of the same level and/or the plurality of third controllers 300 of the lower level, And distributing the second control information to the corresponding second controller 200 and/or the third controller 300.

Optionally, when the second controller 200 is a macro base station level controller, the second controller 200 mainly performs computational offload cooperative optimization control based on a macro base station perspective. The second controller 200, when receiving the first control information distributed by the first controller 100, generates a plurality of macro base station level controllers of the same level and/or a plurality of third controllers 300 of the lower level according to the first control information. The second control information for offloading control is calculated, and the second control information is distributed to the corresponding plurality of macro base station level controllers and/or the third controller 300.

As an example, referring to FIG. 9, when the mobile computing offload cooperative controller is a macro base station level controller, the macro base station level controller and the upper level mobile computing offload cooperative controller interface submodule complete the macro base station level controller and The control information exchange between the global controllers; and the next-level mobile computing offload cooperative controller interface sub-module mainly completes the control information interaction between the macro base station level controller and the micro base station level controller; the same level mobile computing offload cooperation The controller interface sub-module completes the control information interaction between the macro base station level controller and other macro base station level controllers; the resource control interface sub-module to which the controller belongs completes the macro base station level controller and the network resources and calculations controlled by the macro The control information of the resource interacts.

Optionally, when the controller topology is consistent with the physical structure of the radio access network, the first type of control mode, in the first type of control mode, each controller in the mobile computing offload cooperative control system can support the level based Collaborative, vertical coordination, horizontal and vertical coordination of computational offload collaborative control methods.

(1) Computational unloading collaborative control method based on horizontal synergy: Computational unloading collaborative control method based on horizontal coordination between controllers refers to the process of calculating and unloading collaborative optimization control by a certain computing unloading cooperative controller for controller clusters. As a cluster head, the controller only cooperates with the controller of the same level to complete the computational offload collaborative optimization control based on the specific optimization target.

(2) Calculation and unloading cooperative control method based on vertical coordination: The calculation and unloading cooperative control method based on vertical coordination between controllers refers to the process of calculating and unloading collaborative optimization control by a certain computing unloading cooperative controller for the controller cluster hair. As a cluster head, the controller only cooperates with the controllers of its lower level to complete the computational offload collaborative optimization control based on specific optimization goals.

(3) Computational unloading collaborative control method based on horizontal and vertical coordination: Computational unloading cooperative control method based on horizontal and vertical coordination between controllers refers to calculation and unloading collaborative optimization control process from a certain computing unloading cooperative controller The controller acts as a cluster head, and cooperates with its subordinate and peer controllers to complete specific optimization purposes. The target calculation is offloading collaborative optimization control.

The above three methods of computing offload cooperative control are described from the perspective of cooperative control between controllers. Since the controller itself includes several functional modules, some functional modules of the controller may have different enabled states. Therefore, combined with computational unloading The control function module of the cooperative controller and the above three control methods select different operating modes of the control function module, and different computing unloading cooperative control modes can be formed.

Typical computational offload cooperative control modes include a centralized control mode, a hybrid control mode, and a fully distributed control mode.

In an embodiment of the invention, the first type of control mode comprises: a centralized control mode. Specifically, in this mode, the user-based computing offloading service request is centralizedly accepted at the global mobile computing offloading cooperative controller, and the global mobile computing offloading cooperative controller may be a global controller and a macro base station control at the MNO. , micro base station controller. For example, the user's mobile computing offloading service request is sent to the global mobile computing offloading coordinator at the MNO, and the global mobile computing offloading cooperative controller uninstalls the service request according to the present calculation, the control mode currently supported by the user (ie, whether self-organizing is supported) The micro cloud collaborative networking and computing offload service mode) and the resource status of the user, and the control mode of operation of the global controller and its subordinate controllers, through the micro cloud to which the user belongs, the micro cell to which the user belongs, and/or the macro cell The mobile computing offloading collaborative controller of the cell performs collaborative control, completes the computing offload optimization control for the mobile computing outload service request of the user, and notifies the user to obtain a manner of calculating the uninstalling service. The centralized control mode is easy to complete the global computing-based mobile computing offloading service request and its optimization control from the perspective of network and application control, and provides a wide coverage of computing offloading services.

Optionally, when the first type of control mode is supported, in the centralized control mode, when the second controller 200 is a macro base station level controller, the third controller 300 may be a micro base station level controller, and the fourth controller The 400 may be a micro cloud cluster head level controller, and perform calculation and offload cooperative control on the mobile computing offload service request. The second controller 200 uniformly accepts all the user calculation offloading service requests in the macro cell, and the third controller 300 and the fourth controller 400 do not generate the computing offload control service service request based on the network resource and the computing resource optimization, and the third The controller 300 and the fourth controller 400 periodically report the network resource status information and the computing resource status information of the controller to the second controller 200, and the second controller 200 generates the basis based on the resource status information. Network resource and/or computing resource optimized computing offload control business service request.

As an example, referring to FIG. 10, FIG. 10 is a schematic diagram of a functional sub-module of a mobile computing unloading cooperative controller based on a centralized control mode according to an embodiment of the present invention. Optionally, in the centralized control mode, the macro base station controller uniformly processes the mobile computing offloading service request of all users in the macro cell, and the micro base station and the micro cloud cluster head level controller do not generate network resources and computing resources themselves. The optimized computing offload control service service request, the micro cloud cluster head controller and the micro base station level controller periodically periodically the network resource status information and the computing resource status letter to which the controller belongs The information of the user and the unloaded service information analysis result is reported to the controller of the macro base station level by the macro base station level controller, and the macro base station level controller initiates the calculation and offload control service service request based on the network resource and/or the computing resource optimization according to the information. The macro base station level computing offloading cooperative controller performs offloading service request information according to the foregoing resource state information and mobile data from the user, based on different optimization objectives, for example, but not limited to, based on minimum user access delay and minimum system total energy consumption, Calculate the minimum backhaul bandwidth resources of the base station (access point) caused by the offload, minimize the bandwidth occupation on the specific link caused by the calculation offload, maximize the hit rate of the calculation of the offload service request, calculate the unloaded computing resources and the transmission cost most. Excellent optimization goal, calculation and unloading optimization service for service requests. The micro base station and the micro cloud cluster head level controller deactivate the control information generation sub-module, that is, deactivate the calculation offload optimization strategy function, and therefore, the calculation result of the calculation offload optimization control strategy can only be obtained from the macro base station level controller, and According to the optimized deployment result, the related calculation offload optimization control is completed. Referring to FIG. 2, when the first type of control mode includes: the centralized control mode, the second controller 200 includes: a second network resource state statistics sub-module 210, a second computing resource state statistics sub-module 220, and a second service proxy sub-module. 230. The second user mobile computing uninstallation information analysis sub-module 240, the second control information generation sub-module 250, and the second distribution sub-module 260.

The second network resource status statistics sub-module 210 is configured to collect current network resource status information of the network to which the third controller 300 belongs and the plurality of third controllers 300 reported by the third controller 300 when the current control mode is the centralized control mode. The current network resource status information of the network to which the plurality of fourth controllers 400 belong is used as the second network resource status information.

The network resource status information includes, but is not limited to, capacity and load of each communication link, energy consumption of each device, and energy efficiency status information.

The second computing resource state statistics sub-module 220 is configured to collect current computing resource state information of the coordinated computing unit to which the third third controller 300 belongs and the plurality of third controllers 300 when the current control mode is the centralized control mode. The current computing resource state information of the coordinated computing unit to which the plurality of fourth controllers 400 are reported is used as the second computing resource state information.

The computing resource status information includes, but is not limited to, a computing capability of the node, a current usage rate of the computing resource controlled by the node, computing resource usage information based on a specific computing resource configuration manner, and computing resource information remaining by the node.

The second service proxy sub-module 230 is configured to receive a computing offloading service request from the user when the current control mode is the centralized control mode, and meet the preset condition in the second network resource state information and the second computing resource state information. And generating a second request, where the second request includes but is not limited to: calculating the uninstallation information identifier.

The second user mobile computing uninstallation information analysis sub-module 240 is configured to acquire the calculated uninstall service information corresponding to the calculated uninstallation information identifier according to the calculated uninstallation information identifier in the second request, and calculate the historical data of the uninstalled service information, and send the user The history information of the requested node generates prediction information related to the calculation of the uninstallation.

Optionally, the second user mobile computing offload information analysis sub-module 240 is optional in the second controller 200 due to system overhead and power consumption issues.

The second control information generating sub-module 250 is configured to generate, according to the calculated uninstallation information identifier and the preset configuration table in the second request, a second corresponding to the current second network resource state information and the second computing resource state information generated scene data. Control information, wherein the second control information is used to control calculation offloading of the plurality of second controllers 200 of the same level marked by the controller and/or the plurality of third controllers 300 of the lower level.

Specifically, the second control information generating sub-module 250 includes: a converting unit 251, configured to uninstall the information identifier and the preset configuration table information according to the calculation in the second request, and the second network resource state information and the second computing resource state information. Generating scene data, converting the request into a specific target-based computational offload optimization problem, the algorithm selection decision unit 252 is configured to perform algorithm selection according to the calculation of the offload optimization problem, and the algorithm unit 253 is configured to generate the The second control information is described.

a second distribution sub-module 260, configured to distribute the second control information to the plurality of second controllers 200 and/or subordinates of the peers marked by the controller identifier in the network pointed to by the unloading optimization deployment result information The third controller 300 is configured to cause the marked controller to cooperatively control the calculation offload according to the second control information and the calculated offload control mode.

As shown in FIG. 2, optionally, the second controller 200 further includes: a second controller control sub-module 270, a second application management sub-module 280, and a second node management sub-module 290. among them,

The second controller control sub-module 270 is configured to complete control of the second controller 200 and the controller cluster control function based on the virtual controller cluster.

The second application management sub-module 280 is configured to store and manage applications supported by the mobile computing offloading collaborative controller, and mainly includes application registration, application call management, and computing application.

The second node management sub-module 290 is used for management of the general node (user terminal) and the collaborative computing unit to which the controller belongs, including but not limited to joining and exiting of the general node and/or the collaborative computing unit.

In an embodiment of the invention, the first type of control mode further comprises: a hybrid control mode.

As an example, referring to FIG. 11, FIG. 11 is a schematic diagram of a functional sub-module of a mobile computing unloading cooperative controller based on a hybrid control mode according to an embodiment of the present invention.

Optionally, in the hybrid control mode, each level of the controller may receive the mobile computing offloading service request from the user, and may also generate a computing offload control service service request based on the network resource and/or the computing resource optimization, and each control The mobile computing offload cooperative optimization control is performed based on a hierarchical structure consistent with the physical structure of the wireless access network. The controllers at all levels include the control information generation sub-module, that is, the function of calculating the unloading collaborative optimization control strategy, and the calculation and unloading cooperative optimization control based on the calculation between the controllers can be completed according to different optimization goals. Each level controller periodically reports the status information of the resources to which the controller belongs, including network resource status information and computing resources. The status information and the user calculate the uninstall service status information for use by the upper level controller to complete the calculation of the offload optimization control. Each controller can support three coordinated control modes: horizontal coordination, vertical coordination, and horizontal and vertical coordination.

In the vertical cooperative control mode, the macro base station level controller controls the micro base station level controller, and can complete the calculation and offload cooperative optimization control of each micro base station based on the control of the macro base station, and the optimization target includes but is not limited to the macro base station level controller. The network load balancing between the controlled microcells, the load balancing of the computing load controlled by each controller, and the calculated traffic balancing of the backhaul link.

In the horizontal coordinated control mode, only the cooperation between the peer computing and offloading cooperative controllers is supported, and the computing offloading collaborative optimization control centered on the user or based on the optimization of computing resources and/or network resources is implemented, and the user can be received by the user. The controller of the service request is a core of the cluster head controller, and cooperates with several peer base station controllers simultaneously covering the user to complete calculation and offload optimization control, for example, the user sends a mobile calculation to a micro base station level controller. When the service request is uninstalled, the controller that receives the user service request may trigger the calculation of the cluster unloading collaborative optimization control by itself, so that the user can access several micro base stations or one of the accessible devices simultaneously The micro base station obtains the computing offload service, which can effectively reduce the access delay of the user to obtain the computing offload service, and improve the computing application service quality experience of the user.

In the control mode based on horizontal and vertical coordination, it is possible to implement a controller based on the calculation and offloading cooperative control between its lower level and the same level controller, complete user-centered or optimize computing resources and/or network resources. Central computing offloading collaborative optimization control, for example, the controller that receives the user service request is a cluster head controller, and performs computational offloading collaborative optimization control for the same-level base station and its next-level controller that simultaneously covers the user, and can pass Collaboration between the cluster head controller, the controller at the same level as the cluster head controller, or its next level controller allows the user to obtain the required computational offload service.

In the embodiment of the present invention, when the control mode is the hybrid control mode, the second control information generating sub-module 250 is further configured to: receive the first control information distributed by the first controller 100, and receive multiple The second control information distributed by the second controller 200.

In an embodiment of the invention, the first type of control mode further comprises: a fully distributed control mode.

As an example, referring to FIG. 12, FIG. 12 is a schematic diagram of a functional sub-module of a mobile computing unloading cooperative controller based on a fully distributed control mode according to an embodiment of the present invention.

Optionally, when the control mode is the fully distributed control mode, the controller that does not cooperate with the controllers calculates the offload optimization control, that is, between the second controller 200, the third controller 300, and the fourth controller 400. The offload cooperative control is not calculated, and the controllers at various levels unload the service request according to the mobile computing from the user or the unloading control service service request based on the network resource and/or the computing resource optimization, based on the network resource and the computing resource state controlled by the controller. Information and user computing offloads service state information, independently controlling how the service is serviced for unloading service requests.

Therefore, in this control mode, each controller has an activated calculation and offloading collaborative optimization control strategy function, and there is no control information interaction between each controller, and each controller can receive a request for computing offloading service from the user, or A computing offload control service service request based on network resource and/or computing resource optimization is generated.

In the embodiment of the present invention, when the control mode is the fully distributed control mode, the second network resource state statistics sub-module 210 is further configured to collect the second controller when the current control mode is the fully distributed control mode. The current network resource status information of the network belongs to the second network resource status information.

The second computing resource state statistics sub-module 220 is configured to collect, as the second computing resource state information, the computing resource state information of the collaborative computing unit to which the second controller 200 belongs when the current control mode is the fully distributed control mode.

The second service proxy sub-module 230 is configured to receive a user request, and generate, when the second network resource state information and the second computing resource state information meet the preset condition, the network and/or the network to which the second controller 200 belongs. The calculation in the collaborative computing unit unloads the third request for control.

In an embodiment of the present invention, the mobile computing offload cooperative control system includes: a plurality of third controllers 300, and the third controller 300 is configured to receive the second control information distributed by the second controller 200 according to the second The controller identifier in the control information, the controlled calculation offload information identifier, and the calculated offload control mode generate control for calculating the unloading of the plurality of third controllers 300 of the same level and/or the plurality of fourth controllers 400 of the lower level. The third control information and the third control information are distributed to the corresponding third controller 300 and/or fourth controller 400.

Optionally, when the third controller 300 is a micro base station level controller, the third controller 300 mainly performs computational offload cooperative optimization control based on the perspective of the present micro base station. The third controller 300, when receiving the second control information distributed by the second controller 200, generates a plurality of micro base station level controllers of the same level and/or a plurality of fourth controllers 400 of the lower level according to the second control information. The third control information for offloading control is calculated, and the third control information is distributed to the corresponding third controller 300 and/or fourth controller 400.

Optionally, referring to FIG. 2, the third controller 300 includes: a third network resource state statistics sub-module 310, a third computing resource state statistics sub-module 320, a third service proxy sub-module 330, and a third control information generating sub-module. 350. The third distribution sub-module 360. among them,

The third network resource status statistics sub-module 310 is configured to collect, according to the current control mode, the current network resource status information of the network of the plurality of fourth controllers 400 and/or the plurality of third controllers 300 of the same level as the first The third network resource status information is reported to the second controller 200.

When the third controller 300 is a micro base station level cooperative controller, the second controller may be an a macro base level cooperative controller, and the fourth controller may be a micro cloud cluster head level cooperative controller, and the third network resource status statistics submodule The 310 receives the network resource status information reported by the micro-cloud cluster head-level cooperative controllers that it controls, and performs statistics and analysis to generate scenario data based on the state of the network resources of the micro-cell level, specifically, but not Limited to: statistical communication links The load flow and statistics of energy consumption and energy efficiency status information of each device, and the above statistical analysis result information is fed back to the macro base station level cooperative controller, so as to complete the calculation and offload cooperative optimization control as the macro base station level cooperative controller.

The third computing resource state statistics sub-module 320 is configured to collect, according to the current control mode, the current computing resource state information of the plurality of fourth controllers 400 and/or the plurality of third controllers 300 of the same level And calculating the resource status information, and reporting the third computing resource status information to the second controller 200.

Specifically, when the third controller 300 is a micro base station level controller, the second controller may be an a macro base level cooperative controller, and the fourth controller may be a micro cloud cluster head level coordinated controller, and the third computing resource status statistics The sub-module 320 receives the computing resource status information reported from the respective micro cloud cluster head level cooperative controllers, and performs statistics and analysis of the computing resource status information based on the micro cell level, and the generated scene data is used as the micro base station level cooperative controller. The input of the third control information generation sub-module 350 implements computational offload optimization control based on the microcell perspective.

The third service proxy sub-module 330 is configured to generate, according to the receiving user request, a third controller 300 for the peer level when the third network resource state information and the third computing resource state information meet the preset condition. And/or a fourth request in the network to which the plurality of fourth controllers 400 of the lower level belong and/or the associated collaborative computing unit performs the optimization control to perform the optimization control.

Specifically, when the third controller 300 is a micro base station level cooperative controller, the second controller may be an a macro base level cooperative controller, and the fourth controller may be a micro cloud cluster head level cooperative controller, and the micro base station level cooperative control Receiving a mobile computing offloading service request from a user, and calculating a network offload control service service request based on the network resource and/or the computing resource optimization generated based on the micro cell-based computing resource state information and the network resource state information, completing the two types Scheduling of service requests. The scheduling processing result is input to the third control information generating sub-module 350 of the present controller.

The third control information generating submodule 350 is configured to receive or not receive the second control information distributed by the second controller 200 according to the current control mode, and generate, according to the third network resource state information and the third computing resource state information, Third control information for controlling the unloading of the plurality of third controllers 300 of the same level and/or the plurality of fourth controllers 400 of the lower level. Further, the third control information generating submodule includes: a converting unit, configured to generate, according to the calculation of the uninstallation information identifier and the preset configuration table information in the third request, and the third network resource state information and the third computing resource state information Scene data, converting the service request into a calculation-unloading optimization problem based on a specific target; an algorithm selection decision unit for performing an algorithm selection according to the calculation of the unloading optimization problem; and an algorithm unit for generating the algorithm after selecting the preset algorithm The third control information.

Specifically, the scenario data generated according to the network resource state information of the micro cell controlled by the micro cell and the scenario data generated by the computing resource state information, and the computing offload control service service request based on the network resource and/or the computing resource optimization in the cell And the user's mobile computing offloading service request, converting the service request into a computing offload optimization problem based on a specific optimization target, giving a calculation of the offload optimization control result, for example, by minimizing user access delay, most Minimize the total energy consumption of the system or the energy used for calculation, maximize the mobile computing offload service request hit rate of the microcell, and minimize the computational traffic load of a specific link (eg, forward link, backhaul link) And maximizing the throughput of the cell as an optimization target, giving a corresponding calculation offload optimization control strategy result, determining the optimal unloading position of the corresponding calculation offload and the calculated offload information data transmission bandwidth of the corresponding transmission link .

a third distribution sub-module 360, configured to distribute the third control information to the plurality of third controllers 300 of the same level and/or the plurality of fourth controllers 400 of the lower level, so that the labeled controller is configured according to the third The control information and the calculation of the offload control mode control the calculation offload.

As an example, as shown in FIG. 9 , when the third controller is a micro base station level cooperative controller, the micro cell based controller and the upper level computing offload cooperative controller interface submodule complete the micro base station level cooperative control. Control information exchange between the device and the macro base station level cooperative controller; and the next level computing unloading cooperative controller interface sub-module completes the control information interaction between the micro base station level cooperative controller and the micro cloud cluster head level cooperative controller The same-level computing unloading cooperative controller interface sub-module completes the control information interaction between the controller and other micro-base station-level cooperative controllers; the resource control interface sub-module of the controller belongs to the micro-base-level cooperative controller and the control thereof The network resources and the control information of the computing resources interact.

Optionally, as shown in FIG. 2, the third controller may further include a third controller control submodule 370, a third application management submodule 380, a third node management submodule 390, and a third user mobile computing offload information analysis. Sub-module 340. among them,

The third controller control sub-module 370 is configured to complete control and manage the controller and controller cluster-based controller control functions. The third application management sub-module 380 is configured to store and manage applications supported by the mobile computing offloading collaborative controller, and mainly includes application registration, application call management, and computing application. The third node management sub-module 390 is configured to perform the management of the general node and the collaborative computing unit to which the controller belongs, including but not limited to the joining and exiting of the general node and the collaborative computing unit.

The third user mobile computing uninstallation information analysis sub-module 340 calculates the mobile user computing application service information of the control area to which it belongs, analyzes the service request change of the application, and changes the computing application service demand of different types of users, and the above statistical and analysis result information Feedback to its second-level controller, as the second-level controller to complete the calculation of the off-load collaborative optimization control. Optionally, when the third controller is a micro base station level controller, the submodule is an optional submodule due to system overhead and power consumption.

It is worth noting that since the micro base station deployment is usually used for hotspot coverage, and the traffic load of the hotspot area may change due to the mobility of the user, the dynamic optimization control strategy of the micro-cell computing offload cooperative controller may be implemented. For example, when the number of users is small, the function of the unloading cooperative controller is turned off, and some functional sub-modules of the controller are deactivated, so as to maximize the utilization of the mobile radio access network resources and reduce the energy consumption of the mobile radio access network. Therefore, the mobile computing offload optimization strategy sub-module of the micro-cell based computing offloading collaborative controller (ie The control information generation sub-module can have two states of activation and deactivation. When the system is in different control modes, the configuration state of the sub-module may be different. When the sub-module is in an active state, the micro-base station layer controller has a calculation offload optimization control function; otherwise, the calculation offload optimization control function can be completed by the macro-base station level controller of the upper level. For example, when the mobile computing offload cooperative optimization policy sub-module of the primary controller is in an active state, the computing offload cooperative control policy distribution sub-module (ie, the distribution sub-module) of the micro-base-level cooperative controller receives the calculation from the controller. Unloading the optimized unloading optimization result given by the optimization control strategy sub-module, and transmitting the result to the relevant micro base station level controller, the micro cloud cluster head level controller, the wireless access point, the radio access control point, and the general node; When the calculation offload optimization control strategy sub-module of the micro base station level cooperative controller is in a deactivated state, the controller receives the calculation unloading optimization control strategy allocation result from the macro base station level coordinated controller, and sends the result information to the relevant The micro cloud cluster head node and the general node.

For example, when the third control information generating submodule 350 of the micro base station level controller is in an active state, the third distribution submodule 360 based on the micro base station level controller receives the third control generated by the third control information generating submodule 350. And distributing the third control information to the associated micro cloud cluster head controller and the node level controller and the associated network resource and computing resource; when the third control information generating submodule 350 of the micro base station level controller is deactivated In the state, the micro base station level controller receives the second control information distributed from the macro base station level controller, and generates multiple micro base station level controllers of the same level and/or multiple micro cloud clusters of the lower level according to the second control information. The calculation of the head controller unloads the third control information for control, and distributes the third control information to the corresponding micro base station level controller and/or micro cloud cluster head level controller and the associated network resources and computing resources.

In the hierarchical structure-based mobile computing offload control system, the macro base station level, the micro base station level, and the micro cloud cluster head level controller respectively control the computing application update of the computing resource to which they belong, due to the setting of the micro base station and the coverage thereof. The user traffic in the hotspot area is related. The members of the micro cloud form a micro-cloud of dynamic networking because of its mobility. Therefore, the support of the computing and offloading cooperative controller of the micro base station level and the micro cloud cluster head level should also be controlled by the computing application. The user traffic of the area and its networking status are related. That is, when the number of users in a micro cloud is small, the micro base station controller to which the micro cloud belongs may remove the micro cloud cluster head controller and the micro cloud composed thereof. Similarly, when the user to which the micro base station level controller belongs and its computational offloading service are small, the macro base station level controller may select to deactivate the computing offloading cooperative controller of the present micro base station level and its computing resources. To this end, the upper-level controller can adopt a software-defined controller control method, and statistically analyze the result according to the network resources reported by the respective controllers and the state information of the collaborative computing resources, and adaptively control whether the controller is controlled by the controller. The activation is optimized, and the collaborative computing resources controlled by the controller are also optimally controlled. Therefore, the upper controller can optimize the number of controllers controlled by the upper controller, as shown in FIG. It is a workflow for optimizing the number of micro base station level cooperative controllers by a macro base station level cooperative controller. The network resource status information may include, but is not limited to, a bandwidth of a specific link, a throughput of a macro cell, a total energy consumption of the system based on the macro cell, and is used for calculating the unloading. The network resource load, the user load traffic of the controller coverage area, and the like, the computing resource status information may include, but is not limited to, the energy consumption used by the device for calculation, the calculation cost and the transmission cost based on the calculation offload, the user's calculated load flow, and the like. . The controller number optimization algorithm may select an evaluation index including, but not limited to, energy consumption of the micro base station, network load balancing between the micro base stations, calculation load balancing, hit rate of the user computing offload service, and access delay as optimization targets. Optimize the number of coordinated controllers.

In an embodiment of the present invention, the mobile computing offload cooperative control system includes: a plurality of fourth controllers 400, configured to calculate an offload control according to a controller identifier in the third control information when the third control information is received The mode and the controlled calculation offload information identifier generate fourth control information that controls calculation offloading of the plurality of fourth controllers 400 of the same level and/or the plurality of node level controllers 500 of the lower level, and the fourth control information Distributed to the corresponding fourth controller 400 and/or node level controller 500.

Optionally, when the fourth controller is a micro cloud cluster head level controller, the fourth controller 400 mainly completes the computational offload collaborative optimization control based on the micro cloud cluster head view. When receiving the third control information, the fourth controller 400 generates a control for calculating the unloading of the plurality of fourth controllers 400 of the same level and/or the plurality of node level controllers 500 of the lower level according to the third control information. The fourth control information, and the fourth control information is distributed to the corresponding fourth controller 400 and/or node level controller 500 and associated network resources and computing resources.

In some embodiments, referring to FIG. 2, the fourth controller 400 includes: a fourth network resource state statistics sub-module 410, a fourth computing resource state statistics sub-module 420, a fourth service proxy sub-module 430, and a fourth control information generator. Module 450, and fourth distribution sub-module 460.

The fourth network resource status statistics sub-module 410 is configured to collect, according to the current control mode, current network resource status information of the network to which the multiple fourth controllers 400 of the same level belong and/or a network of the plurality of node level controllers 500 of the lower level. The current network resource status information is used as the fourth network resource status information, and the fourth network resource status information is reported to the third controller 300.

Specifically, taking the fourth controller 400 as a micro cloud cluster head level controller as an example, the node receiving the micro cloud member from the control thereof calculates the network resource status information reported by the unloading cooperative controller, and performs statistics and analysis on the network controller. Generating scene data based on the state of the network resource of the micro cloud cluster head level. Specifically, the network resource status information includes, but is not limited to, the traffic of each communication link in the micro cloud, the hop count of the communication between the nodes, and the communication cost thereof. The energy consumption status information of the micro cloud member device; the micro cloud cluster head level controller feeds back the analysis result information of the network resource status information to the micro base station level cooperative controller and/or the micro cloud cluster head level controller, so as to serve as the micro base station The level controller and/or the micro cloud cluster head level controller complete the basis of the computational offload collaborative optimization control.

The fourth computing resource state statistics sub-module 420 is configured to collect, according to the current control mode, current computing resource state information of the coordinated computing unit to which the multiple fourth controllers 400 of the same level belong, and/or multiple node-level controllers 500 of the lower level. Place The current computing resource state information of the collaborative computing unit is used as the fourth computing resource state information, and the fourth computing resource state information is reported to the third controller 300.

Specifically, the fourth controller 400 is used as an example of the micro cloud cluster head level controller, and receives computing resource state information from each micro cloud member, and performs statistics and analysis of the computing resource state information based on the micro cloud, and the generated scenario. As the input of the micro-cloud computing offload collaborative optimization control strategy sub-module, the data implements the computational offload collaborative optimization control strategy based on the micro-cloud perspective. Optionally, the micro cloud cluster head controller can also calculate the computing application service information of the micro cloud member to which it belongs, analyze the request change information of the computing application, and change the computing application service demand of different types of users; and the statistical analysis result information. The feedback is fed to the micro base station level cooperative controller, which serves as the basis for the calculation and offloading collaborative optimization control of the micro base station level cooperative controller.

The fourth service proxy sub-module 430 is configured to receive or not receive the user request according to the current control mode, and generate a fifth request when the fourth network resource state information and the fourth computing resource state information meet the preset condition, where The fifth request includes a user request and/or a request for collaborative optimization control of computational offloading of the plurality of fourth controllers 400 and/or the plurality of node level controllers 500 of the peers.

Specifically, taking the fourth controller 400 as a micro cloud cluster head level controller as an example, the micro cloud level controller generates network resource based and/or based on network resources and/or computing resource state information of each node in the micro cloud. Or calculating a resource optimization centered computing offload control service service request, receiving a mobile computing offload service request from the user, and completing scheduling processing on the two types of service requests based on the scheduling rule. The scheduling processing result is input to the fourth control information generating sub-module 450 of the present controller.

The fourth control information generating sub-module 450 is configured to receive or not receive the third control information that is distributed by the third controller 300 according to the current control mode, and generate, according to the fourth network resource state information and the fourth computing resource state information, Fourth control information for controlling the plurality of fourth controllers 400 of the same level and/or the cooperative computing units of the plurality of node level controllers 500. Further, the fourth control information generating sub-module 450 includes a converting unit 451, configured to perform, according to the calculation of the fourth request, the uninstallation information identifier and the preset configuration table, and the fourth network resource state information and the fourth computing resource. The status information generates scene data, and the service request is converted into a calculation-based unloading optimization problem based on a specific target. The algorithm selects a decision form 452 for performing an algorithm selection according to the calculation of the unloading optimization problem, and the algorithm unit 453 is configured to select a preset. After the algorithm, the fourth control information is generated.

Specifically, taking the fourth controller 400 as a micro cloud cluster head level controller as an example, the scene data generated by the scene data generated by the network resource state information in the micro cloud and the scene data generated by the computing resource state information, and the micro cloud The network resource and/or computing resource optimized computing offload control service service request and the mobile computing offloading service request from the user, the request information is converted into a computational offload optimization problem of a specific optimization target, and the appropriate algorithm is determined and selected based on The optimization algorithm gives the results of the computational offload optimization control, for example, the optimization objectives include, but are not limited to, minimizing users within the micro cloud. Access delay, minimize the total energy loss of the micro cloud, maximize the computational offload request hit rate of the micro cloud, minimize the traffic load of a specific link in the micro cloud, maximize the throughput of the micro cloud, maximize / Minimize the computational offload data transmission bandwidth of a particular transmission link.

The fourth distribution sub-module 460 is configured to distribute the fourth control information to the controller marked by the controller identifier, so that the marked controller controls the calculation uninstallation.

Specifically, when the fourth controller 400 is a micro cloud cluster head level controller, the fourth distribution submodule 460 based on the micro cloud cluster head level distributes the foregoing optimization control policy result to each user node level controller in the micro cloud. . Generally, the real-time distribution is based on the optimization strategy result corresponding to the user's mobile computing offloading service request, and the optimization control result of the computing offloading collaborative control centered on the network resource and/or the computing resource optimization, the micro cloud cluster head controller is usually selected in the network. Active calculation and offload optimization control in the micro cloud during off-peak hours of traffic.

When the fourth controller is a micro cloud cluster head level controller, the micro cloud cluster head level controller and the upper level computing offload cooperative controller interface submodule complete the control information interaction between the controller and the micro base station level controller. And the next-level computing unloading co-controller interface sub-module completes the control information interaction between the micro-cloud cluster head controller and the micro-cloud member node controller; the same-level computing unloads the cooperative controller interface sub-module to complete the controller The control information exchanges with other micro cloud cluster head controllers; the resource control interface submodule completes the micro cloud cluster head controller to interact with the control information of the network resources and computing resources controlled by the micro cloud cluster.

Optionally, referring to FIG. 2, the fourth controller 400 may further include a fourth controller control submodule 470, a fourth application management submodule 480, a fourth node management submodule 490, and a fourth user mobile computing uninstallation information. Analysis sub-module 440. among them,

The fourth controller control sub-module 470 is configured to complete the control of the micro cloud cluster head level controller and the virtual controller cluster based controller control function. The fourth application management sub-module 480 is configured to store and manage applications supported in the controller, and mainly includes application registration, application call management, and supported applications. The fourth node management sub-module 490 is used by the controller to manage the general node and the collaborative computing unit to which the controller belongs, including but not limited to the joining and exiting of the general node and the collaborative computing unit. The fourth user mobile computing offload information analysis sub-module 440 may be configured to extract calculated offload service information from the user mobile computing offload service request received by the fourth controller, perform analysis based on historical data, and provide prediction information and The result of the information aggregation, and the scenario data based on the user mobile computing offloading service request is generated, and the fourth control information generating sub-module in the controller is used as the basis for calculating the unloading collaborative optimization control.

It is worth noting that due to the dynamic nature of the micro cloud networking, that is, the variability of the micro cloud cluster head itself and its members of the micro cloud may exit/join the micro cloud at any time, therefore, a single micro cloud member may also Seen as a micro cloud cluster head. In addition, due to the energy consumption limitation of the micro cloud cluster head, a dynamic control strategy can be implemented on the controller function of the micro cloud cluster head level, for example, when the user does not use the computing application function, the function of the computing unloading cooperative controller is turned off. Activate the calculation uninstallation association Part of the controller's functional modules, etc., in order to maximize their battery life. Therefore, the micro-cloud cluster head level-based mobile computing offload cooperative controller is related to the calculation offload control related computational offload cooperative optimization strategy sub-module (ie, control information generation sub-module), and the calculation off-load cooperative control policy distribution sub-module (distribution sub-module) The computing resource status statistics sub-module and the service agent sub-module may have two states of activation and deactivation. For example, when the computational offloading collaborative optimization strategy sub-module of the current controller is in an active state, the module can complete the computational offloading collaborative optimization control based on the micro cloud cluster head level, and distribute the optimization result through the computing offloading collaborative control policy distribution submodule. Sended to the relevant user node; when the micro-cloud cluster head level computing offload collaborative optimization strategy sub-module is in the deactivated state, the local controller no longer completes its calculation and off-load cooperative optimization control policy function, and only receives the control from the micro base station level The calculation sent by the device unloads the collaborative control deployment result and sends the result to the relevant general node (user terminal). Therefore, correspondingly, taking the fourth controller 400 as an example, when the fourth control information generating sub-module 450 of the micro cloud cluster head level controller is in an active state, the fourth control information generating sub-module 450 may generate fourth control information. And transmitting the fourth control information to the relevant node level controller 500 through the fourth distribution submodule 460; when the fourth control information generation submodule 450 is in the deactivated state, the micro cloud cluster head level controller is no longer completed. The fourth control information generates the function of the sub-module 450, but receives only the third control information distributed from the micro-base station level controller of the upper level thereof, and distributes the third control information to the controller identifier (for example, the controller) The address and/or identification of the controller is marked so that the marked controller performs optimal control over the associated computational offload.

As an embodiment, the computational offloading cooperative controller of the micro cloud cluster head level may also be offloaded based on the calculation of the micro base station level. As shown in FIG. 14, the micro base station level computing offload cooperative controller can optimize the number of computational offloading cooperative controllers of the micro cloud cluster head level controlled by the micro base station level. The network resource status information based on the micro cloud level may include, but is not limited to, a bandwidth of a specific link in the micro cloud, a total throughput of the micro cloud, a total energy consumption of the micro cloud, and a network load traffic used for calculation in the micro cloud. The computing resource status information may include, but is not limited to, energy consumed by the device in the micro cloud for computing, computational cost and transmission cost based on computational offloading, calculated offloading load traffic of users in the micro cloud, load used to calculate offload on a particular link flow. The controller number optimization algorithm may select, but is not limited to, the total traffic of the micro cloud user's computing offload, the total energy consumption of the micro cloud, the energy consumption of the micro cloud cluster head level collaborative controller, and the network load balancing between the micro clouds and/or Or calculate the load balancing, user computing offload service hit rate, access delay and other evaluation indicators as optimization targets, in order to optimize the number of micro cloud cluster head level collaborative controllers.

In the embodiment of the present invention, the mobile computing offload cooperative control system includes: a plurality of node level controllers 500, and the node level controller 500 is configured to acquire, according to the fourth control information, the calculated uninstallation information corresponding to the calculated calculated uninstallation information identifier. In the collaborative computing unit corresponding to the node level controller, the calculation and unloading of the collaborative computing unit corresponding to the node level controller 500 is controlled according to the calculated offload control mode in the fourth control information.

Optionally, in order to support the system's computational offload collaborative control function, the user node also needs to be set and unloaded. Collaborative control of related functional sub-modules, therefore, node-level mobile computing offloading collaborative controllers can be set up in user nodes.

As an example, referring to FIG. 15, FIG. 15 is a schematic diagram of a functional structure of a node-level mobile computing offloading cooperative controller according to an embodiment of the present invention. The function sub-module included in the node-level mobile computing offloading cooperative controller includes: a service proxy sub-module, Resource status information statistics and analysis sub-module, calculation application management sub-module, node management sub-module, user calculation offload service information analysis sub-module, resource control interface sub-module to which the node belongs, interface sub-module of the local node and other general nodes, An interface sub-module between the node and the upper-level collaborative computing unit, and an interface sub-module of the mobile computing unloading cooperative controller. among them,

Service Agent Sub-module: This sub-module calculates and analyzes the resource usage analysis result information of the sub-module based on the resource status information, and calculates the unloading cooperative controller to issue a mobile computing uninstall service request to the node to which it belongs.

Resource status information statistics and analysis sub-module: This sub-module collects the network resources of the node and the state information of the computing resources, and analyzes the state information of the network-related resources and computing resources, and reports the resource status information to the previous one. The mobile computing unloads the collaborative controller and the computing offload optimization strategy sub-module of the node; the network resources include but are not limited to the energy of the node, the current power consumption, and the energy-efficiency state information, and the computing resource information mainly includes but is not limited to the collaboration of the node. The computing unit's computing power, computing resource usage information, and computing resource remaining amount information.

The calculation of the unloading optimization strategy sub-module: the sub-module is based on the computing resource, the energy consumption state and the communication state information of the node itself and the information given by the user computing the unloading service information analysis sub-module given by the resource state information statistics and analysis module, The node evaluates the computing application to be executed, and gives the evaluation result of whether the specific node performs local execution or uninstall based on the specific application to be executed. When the application is determined to be based on local execution, the service proxy submodule of the node is not The user mobile computing uninstall service request needs to be sent; otherwise, when the application needs to be executed based on the uninstallation, the result information is sent to the service proxy sub-module, and the service proxy sub-module sends a corresponding corresponding to the computing unloading cooperative controller to which the node belongs. User Mobile Computing Unloads Service Request.

Computing application management sub-module: This sub-module includes an application registry, an application manager and an application, an application registry for storing and managing all applications supported by the node, and an application manager for performing the collaborative unloading unit and the uninstalled node when performing the unloading The application data is called between; the application refers to all computing applications supported by this node.

Node management sub-module: This sub-module is used for communication interaction between control information and data information between the local node and other general nodes and the upper-level controller.

The user computing unloading service information analysis sub-module: the sub-module is configured to analyze related scenario information when the user requests the mobile computing offloading service, and output related user computing unloading service information, and the user computing the uninstalling service information mainly includes the user's mobility. Information and mobility information, geographic location information, and user computing application preferences; this information will be used as a compute offload optimization strategy sub-module for the node controller for mobile computing offload optimization Forecast data and optimization basis

The resource control interface sub-module to which the node belongs: the interface sub-module is used to complete the control information interaction between the resource state information statistics and analysis module and the resource management module to which the node belongs;

The interface sub-module of the node and other general nodes: the interface sub-module is used to complete communication and networking related information interaction between the node and other general nodes;

An interface sub-module between the node and the upper-level collaborative computing unit: the interface sub-module is used to complete data information interaction between the node and other collaborative computing units to which the upper-level controller belongs;

An interface sub-module for the mobile computing offloading cooperative controller: the interface sub-module is configured to complete the calculation and unloading related control information interaction between the node and the mobile computing offloading cooperative controller to which the node belongs;

The general node controls the network resources to which it belongs and the computing resources of the collaborative computing unit. The corresponding four functional sub-modules and their functions are as follows:

The node and the network-related resource management sub-module: complete the management of the network-related resources controlled by the node.

The node and the network-related resource sub-module: refers to the network resources controlled by the node, including but not limited to the node resource and the link resource; the node resource may include, but is not limited to, the endurance capability and power consumption of the node.

The computing resource management sub-module of the node refers to the computing resource management controlled by the node, including but not limited to managing the computing capability of the collaborative computing unit to which the node belongs, the computing resource usage rate, the computing application information in the computing resource, and the energy The computing application service and the optimization management mechanism based on the computing application are provided, wherein the optimization management mechanism based on the computing application includes, but is not limited to, a computing application update mechanism of the node.

The computing resource sub-module of the collaborative computing unit to which the node belongs: refers to the computing resource of the collaborative computing unit controlled by the node. Because the node controller and the collaborative computing unit can be separated, the computing resource can be a local computing resource on the node, or a remote collaborative computing resource to which the node belongs. At the same time, the collaborative computing unit can support dynamics for the node. Home control, ie a collaborative computing unit can be controlled by different general nodes and/or computational offloading cooperative controllers at different times.

It is worth noting that for a general node, a general node can support the above functional modules, and can also simplify the above functional modules, and only support the functions of some of the functional modules, so as to adapt to some terminal nodes with simple functions and low power consumption. Limitations.

It is worth noting that the controller layered control architecture and the physical computing offload node network of the mobile computing offload control system composed of the first control 100, the second control 200, the third controller 300, and the fourth controller 400 are divided. Whether the layer architecture is consistent, multiple controllers usually work together in a hierarchical control manner. Therefore, in order to avoid the generality, in the mobile computing unloading cooperative controller based on the hierarchical control, taking the nth-level mobile computing unloading cooperative controller as an example, as shown in FIG. 9, the mobile computing unloading cooperative controller also has the function. Modules include:

And the upper-level computing unloading co-controller interface sub-module: the sub-module is used to complete the information interaction between the computing unloading co-controller and the upper-level computing unloading co-controller, and the interaction information includes but is not limited to the upper-level control. The optimal control result of the controller and the computing unloading cooperative controller to calculate the network resource and computing resource status information related to the calculation and unloading cooperative control sent by the unloading cooperative controller, the user computing unloading service information, and the controller Collaborative control information related to computational offloading between the upper level computing offload coordinator.

And the next-level computing unloading co-controller interface sub-module: the sub-module is used to complete the information interaction between the computing unloading co-controller and the next-level computing unloading co-controller, and the interaction information includes but is not limited to the controller pair The calculation and offloading cooperation control result of the next-level controller and the network resource and computing resource state information related to the calculation and offloading cooperative control from the next-level controller, the user computing the uninstallation service information, and the relationship with the next-level controller Calculate the offload related collaborative control information.

The same level computing unloading collaborative controller interface sub-module: This sub-module is used to complete the collaborative control information interaction between the controller and other computing unloading collaborative controllers of the same level. The information of the interaction includes, but is not limited to, the calculation and unloading cooperative control result completed by the cooperative control between the controller and the peer controller, and the network resource and computing resource state information related to the calculation and offload cooperative control between the controllers at the same level, and the user calculation Unloading service information and collaborative control information related to computational offloading with peer controllers.

The resource control interface sub-module of the controller belongs to: the sub-module is used to complete the control information interaction between the network resource and the collaborative computing unit resource of the controller, and the interaction control information includes but is not limited to the network resource management module of the controller. Collecting network resource status information, collecting computing resource status information of the collaborative computing unit from the associated computing resource management module, and calculating and offloading optimization control performed by the controller on the computing resource through the interface and optimal control of the network resource to which the network resource belongs.

Specifically, the optimized control for the computing resource includes, but is not limited to, an application update mechanism based on the popularity of the computing application in the user mobile computing offloading service request, and the optimized control for the network resource includes but is not limited to the mobile wireless access network. Optimized control of backhaul and preamble link bandwidth resources and optimal control for energy consumption of wireless access network nodes.

The mobile computing offloading cooperative controller controls the mobile radio access network resources to which it belongs and the computing resources of the collaborative computing unit. This part does not belong to the mobile computing offloading cooperative controller itself, and the corresponding four functional submodules and their functions They are as follows:

The mobile radio access network resource management sub-module refers to the resource management of the mobile radio access network and the general node controlled by the controller;

The mobile radio access network resource sub-module refers to the mobile radio access network resource controlled by the controller, including node resources and link resources;

The computing resource management sub-module of the associated collaborative computing unit refers to the management of the computing resources controlled by the controller, including but not limited to computing power, computing resource usage rate, computing application information in computing resources, and calculations that can be provided. An application service and an optimization management mechanism based on a computing application, wherein the optimization management mechanism based on the computing application includes, but is not limited to, a computing application update mechanism in the control system;

The computing resource sub-module of the associated collaborative computing unit: refers to the computing resource of the collaborative computing unit controlled by the controller. Since the controller and the collaborative computing unit can be separated, the computing resource may be a computing resource of the local collaborative computing unit controlled by the controller, or may be a computing resource of the remote collaborative computing unit, and the collaborative computing unit may support the The dynamic home control of the specific computing offload controller, that is, a certain collaborative computing unit can be controlled by different general nodes and/or computing offloading cooperative controllers at different times.

In an embodiment of the present invention, the mobile computing offload cooperative control system includes: a virtual controller cluster generation module 600, configured to generate a combination of different virtual controller clusters based on at least two preset controllers, and control mobile computing The offloading collaborative control system switches between different combinations of virtual controller clusters, and the mobile computing offloading cooperative control system controls the computing offloading of controllers in different virtual controller cluster combinations according to the first request, wherein different virtual controllers The preset controllers included in the combination of clusters are different.

In the embodiment of the present invention, the virtual controller cluster generation module 600 is further configured to: when the control mode is the second type of control mode, that is, when the controller topology is inconsistent with the physical calculation offload node topology of the mobile radio access network, based on The software-defined controller architecture supports a virtual mobile computing offloading collaborative controller architecture, that is, when the mobile computing offloading collaborative control system supports a specific optimization target, it can temporarily form an offloading collaborative controller with a specific mobile computing For the cluster head of the controller, the controller and its associated mobile computing offload the collaborative controller as a virtual controller cluster of controller members, and generate a combination of different virtual controller clusters based on at least two preset controllers.

In the embodiment of the present invention, the virtual controller cluster generating module 600 includes: an obtaining submodule 610, configured to acquire a controller corresponding to the controller identifier that receives the first request. The determining sub-module 620 is configured to determine whether the computing uninstallation information corresponding to the calculated uninstallation information identifier in the first request exists in the coordinated computing unit of the corresponding controller. The determining submodule 630 is configured to determine the corresponding controller as the target controller when there is no computing resource corresponding to the unloading information identifier calculated in the first request. The configuration sub-module 640 is configured to configure the target controller as a cluster head controller of the virtual controller cluster, and select a controller associated with the target controller as a member controller of the virtual controller cluster.

In the embodiment of the present invention, the cluster head controller of the virtual controller cluster may be a macro base station level controller, or may be a micro base station level controller, which is not limited thereto.

Specifically, the controller that receives the computing offload cooperative control service service request centered on the network resource and/or the computing resource optimization or the user-centered computing offload service request is a cluster head of the controller, The associated controller is a cluster member, and a temporary virtual controller cluster can be formed. This virtual controller cluster can be followed Based on the centralized control mode, that is, the virtual controller cluster head completes the calculation and unloading optimization decision function, each cluster member controller only completes the statistics and analysis functions of network resources and computing resources, and calculates the function of the offload optimization control policy distribution; optimization control After the end, the virtual controller cluster will be disbanded. According to the above work process based on the virtual controller cluster, the cluster head controller needs to complete the mode control process based on the virtual controller cluster.

The mode control process of the virtual controller cluster is used as part of the controller control function to complete the storage and update of control information interaction and related control state information between the controllers in the virtual controller cluster.

When the controller receives a computing offloading service request from a user and/or a computing offloading control service service request from network resources and computing resource optimization, and the controller supports the function of optimizing control based on the virtual controller cluster, The virtual controller cluster control function of the controller is triggered, and the controller becomes the cluster head controller based on the virtual controller cluster computing offload optimization control. The cluster head controller queries and obtains each controller and its controller identifier associated with the controller through the controller state information storage submodule, and then starts the controller mode control information interaction module to selectively associate with the controller. The controller sends the virtual controller cluster association information, where the information includes the identification number of the virtual controller cluster to be formed this time. The controller receives an association response message from the virtual controller of its associated controller, and sends a virtual controller cluster invitation message to the controller that sends the association response message, and the controller makes an invitation response message according to the received association controller. The associated controller joins the virtual controller cluster whose cluster head is the cluster head and whose identification number is the identification number, and completes the formation process of the virtual controller cluster. Then, the controller performs an initialization process of the virtual controller cluster on the virtual controller cluster. After the initialization process of the virtual controller cluster is completed, the cluster head controller enters the mobile computing offload collaborative optimization control process based on the virtual controller cluster, and completes the mobile computing offload collaborative optimization control based on the specific optimization target.

Specifically, first, the cluster head controller updates controller state information based on the network resource state of the virtual controller cluster and the computing resource state, and stores the updated control state information in the controller state information storage submodule. The control state information includes, but is not limited to, a real-time control relationship topology diagram between the cluster head controller and the member controllers in the virtual controller cluster, including but not limited to a layered controller control topology diagram based on a physical network topology and currently a control topology diagram of a virtual controller cluster formed on the controller; in addition, but not limited to, an enabling information and an allowed operation information corresponding to resources controlled by respective controllers associated with the virtual controller cluster, that is, For the virtual controller cluster head, which resources are opened by the controller to which virtual controller cluster, corresponding to which control functions the virtual controller can perform. After the update process of the above control information is completed, the mobile computing offload optimization control optimization target and its constraint condition based on the current virtual controller cluster are constructed according to the above update information, and the cluster head controller gives the mobile computing offload optimization based on the optimization target. The policy distributes the optimization control result information to each member controller controlled by the virtual controller cluster, and each member controller completes the operation of unloading the relevant calculation according to the distribution result.

After the cluster head controller completes the optimization control based on the specific optimization target, the virtual controller cluster performs the dissolving process, The virtual controller cluster head sends the disbanding information of the virtual controller cluster to each virtual controller member through the controller mode control information interaction module, and each virtual controller member answers the disbanding information, and each controller deletes the virtual controller. The associated controller state information of the cluster, that is, the control topology map corresponding to the virtual controller cluster and its associated control state information will be deleted.

Through the virtual controller cluster-based computational offload optimization control process generated when the control mode is the second type of control mode, the mobile computing offload collaborative optimization control based on the specific computing offloading collaborative controller can be completed, and the mobile computing offload cooperative control is improved. The flexibility of the method. Optionally, referring to FIG. 2, the mobile computing offload cooperative control system further includes: a control mode configuration module 700, configured to use the first controller 100, the plurality of second controllers 200, and the plurality of third controllers 300, The fourth controller 400, and the control modes of the plurality of node level controllers 500 are configured, and the configured control mode is written into the preset configuration table.

By pre-configuring the mode of the controller and writing the configured control mode to the preset configuration table, the computing offload cooperative control system can flexibly support coordinated control based on centralized, fully distributed and hybrid controllers. Ways to improve the scalability and flexibility of the mobile computing offload collaborative control method.

In addition, when configuring the control mode, you can choose to configure the control topology of the controller in a centralized or distributed manner. When the mobile computing unloading cooperative control system is initialized, the global controllers outside the MNOs can be used to select and configure the control modes supported by the respective controllers, or the respective control modes can be selected and configured when each controller is initialized. In different control modes, the function of the computational offload cooperative control function module of the macro base station level controller, the micro base station level controller, and the micro cloud cluster head level controller and the cooperative control mode supported by the controller are different.

As an example, in the mobile computing offload cooperative control system, the user may select different working modes. In the user terminal initialization phase, the node level controller in the user terminal may initiate a control working mode process based on the mobile computing offload cooperative control, and select A mobile computing offload cooperative control working mode supporting an ad hoc network or a mobile computing offload cooperative control working mode not supporting an ad hoc network. Based on the computing offload collaborative control working mode selection configuration, the software-defined mobile computing offloading collaborative control system can complete the mobile computing offloading service request from the user and the computing offload control service service request based on the network resource and/or computing resource optimization Through the computational offloading based on the edge device, optimizing the utilization of the network resources and the computing resources to which the controller belongs, and completing the reduction of the pre-transmission and return-back links caused by the data center that is offloaded to the MNO to calculate the bandwidth overhead of the offload and reduce the user. The service delays, balancing the traffic load between different base stations and other optimization goals. In the following, from the perspective of the user acquiring the mobile computing uninstall service, a method for the user to obtain the calculated uninstall service in the control system is given.

When the user supports the micro-cloud networking based on the self-organization, in this mode, the user can obtain the mobile computing unloading service through the computing resources of the micro cloud, the micro cell, and the macro cell. The control mode of the corresponding mobile computing offload cooperative control system may be the calculation offload cooperative control party when the controller topology is consistent with the physical calculation offload node topology of the mobile radio access network. (the first type of control mode), that is, the controller can support the control mode based on centralized, hybrid, and distributed control, or it can be based on the virtual topology when the controller topology is inconsistent with the physical calculation of the unloading node topology of the mobile radio access network. The control mode of the controller cluster.

When the controller topology is consistent with the physical computing offload node topology of the mobile radio access network, and the user acquires the computational offload service method using the centralized control mode, the mobile computing offload cooperative control system can uniformly accept the mobile computing from the user through the macro base station level controller. The service offload cooperative optimization policy functions of the micro base station level and the micro cloud cluster head level controller are deactivated, and the network resource status statistics submodule and the computing resource status statistics submodule of the micro cloud level and the micro base station level controller respectively The analysis result data of the feedback network resource status information and the calculation resource status information is sent to the macro base station level controller, and the macro base station controller performs mobile computing offload collaborative optimization control based on the network resource and the computing resource state statistical analysis information in the area of the macro cell, The optimization control result is offloaded based on the calculation of the macro cell, and is distributed to the micro base station level controller and the micro cloud cluster head controller through the distribution sub-module. Specifically, the macro base station level controller receives the calculation offload service request of the user, extracts the calculation offload information identifier required in the service request, and determines whether the calculation offloading of the service request can be supported in the collaborative computing unit of the macro base station level controller. Service, if yes, the macro base station controller notifies the user to obtain the calculation offload service from the collaborative computing unit of the macro base station; otherwise, the macro base station controller determines whether the mobile computing offload service request upload is supported, and if the support service request is uploaded, the macro base station level The controller uploads the service request to the global controller outside the MNO, and notifies the user to obtain the computing uninstall service from the global controller outside the MNO. If the upload of the unloading service request is not supported, the macro base station level controller can support the Calculating the controller of the offloading service and the supported computing offloading related information, and collecting network resource and computing resource state information based on the controller and its computing resources controlled by the controller, and converting the service request into a mobile computing offloading collaborative optimization problem And based on the control information of the macro base station level controller The sub-module gives the mobile computing offload optimization result, distributes the optimization result information to the controlled controller and its computing resources through the distribution sub-module, and the controlled controller and/or its collaborative computing unit performs the mobile computing unloading operation, and The macro base station level controller notifies the user to obtain the required mobile computing offload service from the designated collaborative computing unit.

When the controller topology is consistent with the physical computing offloading node topology of the mobile radio access network, and the user acquiring the computing offloading service method adopts the hybrid control mode, the user terminal sends a mobile computing offloading service request to the micro cloud cluster head controller as an example. The user sends a mobile computing offload service request to the micro cloud level controller. First, the micro cloud cluster head level controller determines whether the requested mobile computing offload service can be supported in other user equipments in the micro cloud, and if so, the micro cloud level control The device uninstalls the information identifier based on the calculation, completes the calculation and offloading collaborative optimization control based on the user in the micro cloud, and distributes the mobile computing offload optimization result to the relevant user terminal, and then notifies the user terminal to obtain from the specified user terminal in the micro cloud. Mobile computing offloading service; otherwise, the micro cloud level controller determines whether the computing offloading service can be obtained from a controller that is horizontally coordinated with the controller based on the controller cooperation, and if so, generates a mobile meter through its control information generating submodule Calculating and unloading the collaborative optimization result, and distributing the optimization result information to the corresponding user terminal through the distribution sub-module, notifying the user to obtain the required mobile computing uninstallation service on the specified user terminal; The computing unloading service, the micro cloud cluster head level controller determines whether the mobile computing uninstall service request upload is supported, and if the mobile computing uninstall service request upload is supported, the mobile computing uninstall service request is uploaded to the upper level micro The base station cooperates with the controller for processing; if the upload of the mobile computing offload service request is not supported, the micro cloud cluster head level controller notifies the user that the mobile computing offload service corresponding to the service request cannot be provided.

When the controller topology is consistent with the physical computing offloading node topology of the mobile radio access network, and the user obtains the mobile unloading service request by using the distributed control mode, the user sends the mobile computing offloading service request to the micro cloud cluster head controller as an example. Sending a mobile computing offloading service request to the micro cloud cluster head level controller, and the micro cloud cluster head level controller determines whether the requested mobile computing offloading service can be supported in the collaborative computing unit to which other user terminals in the micro cloud belong. If yes, the micro cloud cluster head controller performs mobile computing offload optimization control based on the service request, obtains an optimization result, and distributes the optimization result information to a user terminal that provides a computing offload service in the micro cloud, and the user terminal performs a calculation uninstall operation. Meanwhile, the micro cloud cluster head controller notifies the user who proposes the mobile computing offload service request to obtain the mobile computing offload service from the user terminal that is calculated to be uninstalled; otherwise, the micro cloud cluster head level controller determines whether to support the upload of the mobile computing offload service request. If the support service requests an upload, then move the user Calculating the unloading service request to upload to the micro-base station level controller of the upper level, and processing by the micro base station level controller; if the upload of the mobile computing offloading service request is not supported, the micro cloud cluster head level controller notifying the user that the user cannot provide the The required mobile computing offload service; when the micro base station level controller processes the uploaded mobile computing offload service request, the micro base station level controller determines whether the mobile computing offload service requested by the user can be supported in the collaborative computing unit to which the present micro base station belongs If yes, the user is notified to obtain the mobile computing offload service from the collaborative computing unit of the micro base station; otherwise, the micro base station level controller determines whether the mobile computing offload service request upload is supported, and if the service request upload is supported, the service request is uploaded. The macro base station level controller is processed by the macro base station level controller; if the service request upload is not supported, the micro base station level controller notifies the user that the computing offload service required for the service request cannot be provided; when the macro base station level controller Macro base station level when processing the uploaded mobile computing offload service request The controller determines whether the computing offload service of the service request can be provided in the collaborative computing unit to which the macro base station belongs, and if yes, notifies the user to obtain the mobile computing offload service from the collaborative computing unit to which the macro base station belongs; otherwise, the macro base station level control The device determines whether the service request upload is supported. If the service request upload is supported, the service request is uploaded to the global controller outside the MNOs, and the global controller controls and notifies the user to obtain the computing uninstall service from the collaborative computing unit to which it belongs. If the macro base station level controller does not support service request upload, the macro base station level controller notifies the user that the mobile computing offload service required for the service request cannot be provided.

When the controller topology is inconsistent with the physical computing offloading node topology of the radio access network, the micro cloud level controller receives the mobile computing offloading service request from the user as an example, and the micro cloud level controller receives the mobile computing offloading service of the user. please Soliciting, based on the controller associated with the neighboring controller, obtaining the controller and the collaborative computing unit information capable of providing the computing unloading service, and establishing a virtual controller cluster with the micro cloud cluster head controller as the cluster head controller; collecting this Based on the network resource and computing resource state information controlled by the virtual controller cluster, the user service request is transformed into a mobile computing offload collaborative optimization problem, and the problem is given based on the control information generation sub-module of the cluster head controller. The optimization result is distributed to the member controllers of the virtual controller cluster, and each controller controls the coordinated computing unit to which the controller belongs to perform the computing unloading operation, and the cluster head controller notifies the user to obtain the computing uninstallation service. After the user obtains the mobile computing uninstallation service, the micro cloud level controller completes the disbanding process of the virtual controller cluster.

When the user does not support the mobile computing offloading cooperative control in the micro cloud based on the ad hoc networking, the control mode of the corresponding mobile computing offload cooperative control system may be based on the controller topology and the physical computing offloading node topology of the mobile radio access network. Time-based mobile computing offload cooperative control mode. On this basis, the controller can support centralized control mode, hybrid-based control mode and distributed-based control mode; it can also be based on controller topology and mobile wireless access. The network controller calculates the control mode based on the virtual controller cluster when the unloading node topology is inconsistent.

As an example, when the user does not support the mobile computing offload cooperative control mode based on the self-organizing networking and its micro cloud, in the centralized control mode, the mobile computing offload cooperative control system can uniformly accept the user from the macro base station level controller. The mobile computing offloading service request, the control information generating submodule of the micro base station level controller is deactivated, the network resource state information statistical submodule of the micro base station level controller, the computing resource state information statistical submodule, and the user mobile computing unloading service information analysis The sub-module respectively feeds back the network resource state information based on the micro base station, the computing resource state information, and the analysis result data of the user mobile computing offloading service information to the macro base station level controller, and the macro base station controller is based on the network resource and the computing resource status of the macro cell. The information analysis data is subjected to mobile computing offload cooperative optimization control, and the mobile computing offload cooperative optimization control result information based on the macro cell is distributed to the micro base station controller through its distribution submodule. Specifically, the macro base station level controller receives the mobile computing offloading service request of the user, extracts the calculated uninstallation information identifier required in the service request, and determines whether the service request can be supported in the collaborative computing unit controlled by the macro base station level controller. Mobile computing offload service, if yes, the macro base station controller notifies the user to obtain the mobile computing offload service from the collaborative computing unit it controls; otherwise, the macro base station controller determines whether to support the service request upload, if the support service request uploads, The macro base station level controller uploads the service request to the global controller outside the MNO, and notifies the user to obtain the mobile computing offload service from the global controller outside the MNO and the coordinated computing unit controlled by the MNO. If the upload of the service request is not supported, The macro base station level controller obtains the controller and the computing resource information capable of supporting the mobile computing offloading, and collects network resources and computing resource status information based on the controller and its computing resources controlled by the macro base station level controller, and converts the service request into A mobile computing offload collaborative optimization problem based on macro base station level control The control information generation sub-module gives the calculation unloading optimization result, distributes the mobile computing offload optimization result information to the controller and the associated collaborative computing unit through the distribution sub-module, and performs the mobile computing offload optimization control operation by the associated computing coordination unit, Macro base station level controller The user is informed that the required mobile computing offload service is obtained from the designated collaborative computing unit.

When the user does not support the unloading collaborative control mode based on the self-organizing networking and its micro-cloud, in the hybrid control mode, all levels of controllers can receive mobile computing offloading service requests from users, and control of each level of controllers. The information generation sub-module is activated, and the macro base station level and the micro base station level controller collect the network resource status information and the calculation resource status information controlled by the macro base station level, and perform statistics and analysis, and each controller can support vertical and/or horizontal based. Collaborative computing offloads collaborative optimization control strategies, so controllers at all levels can work in accordance with different hybrid collaborative optimization control methods. After the controller obtains the calculated offload optimization control result, the calculation offload cooperative optimization control result information is distributed to the corresponding controller and its collaborative computing unit. For example, when the user sends a mobile computing offloading service request to the micro base station level controller, when the user sends a mobile computing offloading service request to the micro base station level controller, first, the micro base station level controller determines whether the cooperation can be performed by the controller. Providing, by the computing unit, a computing offloading service of the service request, if yes, the micro base station level controller notifying the user to obtain the mobile computing offloading service from the collaborative computing unit to which the micro base station belongs; otherwise, the micro base station level controller is based on the controller cooperation. Determining whether the mobile computing offloading service can be obtained from the collaborative computing unit to which the controller with which it cooperates, and if so, generating the collaborative optimization control result through its control information generating submodule, and distributing the result to the controlled In the collaborative computing unit, the user is notified to obtain the required mobile computing offload service in the designated collaborative computing unit; if the mobile computing offload service cannot be completed through the controller cooperation, the micro base station level controller determines whether the service request upload is supported. If the support service requests to upload, please use the service It is uploaded to a macro base station controller for processing; If not upload service request, the femto base station according to the present level controller notifies the user can not provide the service requesting mobile computing unloading service.

When the user does not support the computational offload cooperative control mode based on the ad hoc networking and its micro cloud, in the full distributed control mode, the macro base station level controller or the micro base station level controller does not perform vertical and/or horizontal coordination, and It is the independent completion of the mobile computing offload optimization control based on its control area. The macro base station level controller or the micro base station level controller respectively collect network resource state information and computing resource state information of the controlled area through the network resource state information statistics submodule and the computing resource state information statistics submodule, and the macro base station level controller Or the micro base station level controller is based on the statistical analysis results of the network resources and the computing resource state information in the area controlled by itself, and generates a sub-module based on its own control information to perform calculation and offloading collaborative optimization control, and gives corresponding calculation and unloading optimization results, and The optimization result information is distributed to the computing resources in the area controlled by the controller through its own distribution sub-module. It is worth noting that since there is no cooperative control between the controllers, when the controller independently services the service request and completes the related calculation and offload control, the resources controlled by it are only their own computing resources, therefore, The problem of low computation hit rate and large response delay is caused by the calculation of the unloading service request. In order to avoid the above problem, the controller may upload the mobile computing uninstall service request that cannot be satisfied by the controller to its upper controller for processing again. Way to improve the hit rate of the service request.

Taking the user sending a mobile computing offloading service request to the micro base station level controller as an example, first, the micro base station level controller determines whether the requested computing offloading service can be supported in the collaborative computing unit controlled by the controller, and if so, The controller notifies the user to obtain the mobile computing offload service from the collaborative computing unit of the micro base station; otherwise, the micro base station level controller determines whether the upload of the service request is supported, and if the upload of the service request is supported, the service request of the user is The macro base station level controller uploaded to its upper level is processed by the macro base station level controller; if the upload of the service request is not supported, the micro base station server notifies the user that the mobile computing offload service cannot provide the service request; macro base station level control When processing the uploaded service request, it first determines whether the mobile computing offload service of the service request can be supported in the collaborative computing unit controlled by the macro base station, and if so, notifying the user to obtain the mobile computing from the collaborative computing unit to which the macro base station belongs Unload the service; otherwise, the macro base station level controller determines whether the service is supported. Request uploading, if the support service request uploads, upload the request to the global controller outside the MNOs, and notify the user to obtain the mobile computing uninstall service from the global controller and its controlled computing resources; if the service request is not supported for uploading The macro base station level controller notifies the user that the mobile computing offload service required for the service request cannot be provided.

When the user does not support the unloading cooperative control mode based on the self-organizing networking and the micro-cloud, and the controller topology is inconsistent with the physical computing offloading node topology of the mobile radio access network, the controller that receives the user mobile computing unloading service request is received. A virtual controller cluster based computational offload collaborative optimization control process can be performed. On the basis of completing the formation process of the virtual controller cluster, the controller completes the computational offloading collaborative optimization process with the controller as the cluster head of the virtual controller, and distributes the optimization result information to each controller of the virtual controller cluster. The optimization control of the calculation and unloading is completed by each member controller and its associated collaborative computing unit. After the calculation of the unloading optimization control is completed, the controller completes the disbanding process of the virtual controller cluster. Specifically, the micro base station level controller receives the mobile computing offloading service request from the user as an example, and after receiving the mobile computing offloading service request of the user, the micro base station level controller establishes the micro based on the controller associated with the mobile base station level controller. The base station level controller is a virtual controller cluster of the cluster head controller; the cluster head controller collects network resources and computing resource status information controlled by the virtual controller cluster, and on the basis of this, converts the service request into a computing uninstallation. Collaborative optimization problem, and based on the control information generation sub-module of the cluster head controller, the optimization result of the problem is given; the cluster head controller distributes the optimization result information to each member controller of the virtual controller cluster, and at the same time, notifies After the user obtains the mobile computing uninstallation service, the cluster controller completes the disbanding process of the virtual controller cluster.

According to the mobile computing offloading cooperative control system of the embodiment of the present invention, when the first request is received, the first controller generates control information corresponding to the state information according to the calculated uninstallation information identifier and the control mode in the first request, and The control information is distributed to the second controller, the third controller, the fourth controller, and the node level controller step by step, and can complete the software-defined mobile computing offload optimization control, flexibly supporting user-centric or network-based resources and / or computing resource optimization as the center of different mobile computing offload optimization goals, improve the mobile computing offload collaborative control party The scalability and flexibility of the law.

FIG. 16 is a schematic flowchart of a mobile computing offload cooperative control method according to an embodiment of the present invention.

Referring to FIG. 16, the mobile computing offload cooperative control method includes:

S141: When generating the first request, collecting current first network resource state information and first computing resource state information reported by the multiple second controllers, acquiring a current control mode from the preset configuration table, and according to the first request Calculating the unloading information identification and control mode in the first control information corresponding to the current first network resource state information and the first computing resource state information generating scenario data, and distributing the first control information to the corresponding controller, wherein The first control information includes, but is not limited to, a controller identifier, a calculation offload control mode, and a controlled calculation offload information identifier.

In an embodiment of the present invention, the types of the first controller, the second controller, the third controller, and the fourth controller may be a global controller, a macro base station level controller, a micro base station level controller, and a micro Any of the cloud cluster head controllers.

In an embodiment of the present invention, the control mode includes: a first type of control mode and a second type of control mode, wherein the first type of control mode is the same as the controller topology and the mobile radio access network physical computing offload node topology The control mode, the second type of control mode is a control mode in which the controller topology and the mobile radio access network physically calculate the unloading node topology.

In an embodiment of the present invention, the plurality of second controllers, and/or the plurality of third controllers, and/or the current network resource status information of the network to which the plurality of fourth controllers belong are collected as the first network resource status information. . Collecting a plurality of second controllers, and/or a plurality of third controllers, and/or current computing resource state information of the coordinated computing units to which the plurality of fourth controllers belong as the first computing resource state information. Receiving a user request, determining whether the first network resource state information and the first computing resource state information meet the preset condition, and when the preset condition is met, generating a first request, where the first request includes, but is not limited to, calculating the uninstallation information identifier . Generating first control information corresponding to the current first network resource state information and the first computing resource state information generating scenario data according to the calculated uninstallation information identifier and the preset configuration table in the first request, where the first control information is used The computational offloading of the controller marked by the controller identification in the network to which the corresponding controller identification and/or prediction information is directed is calculated. Distributing the first control information to the controller marked by the controller identifier in the network pointed to by the collaborative computing unit and/or the corresponding controller identifier and/or prediction information, so that the marked controller is based on the first control information And the calculation of the unloading control mode controls the calculation and unloading. Calculating the unloading information corresponding to the calculated uninstallation information identifier according to the calculated uninstallation information identifier in the first request, and calculating the unloading-related prediction information based on calculating the historical data of the uninstallation and transmitting the history information of the node requested by the user.

In some embodiments, referring to FIG. 17, before step S141, the method further includes:

S151: a first controller, a plurality of second level controllers, a plurality of third controllers, a plurality of fourth controllers, and The control mode of multiple node level controllers is configured, and the configured control mode is written into the preset configuration table.

In this step, by configuring the mode of the controller in advance and writing the configured control mode into the preset configuration table, the mobile computing offload cooperative control system can flexibly support centralized, fully distributed, hybrid And the controller cooperative control mode based on the virtual controller cluster improves the scalability and flexibility of the mobile computing offload collaborative control method.

S142: Receive first control information that is distributed by the first controller, and generate multiple second controllers and/or subordinates of the same level according to the controller identifier in the first control information, calculate the uninstallation information identifier, and calculate the offload control manner. The computing of the plurality of third controllers unloads the second control information that is controlled, and distributes the second control information to the corresponding second controller and/or third controller.

In the embodiment of the present invention, the first type of control mode includes: a centralized control mode, and when the control mode is the centralized control mode, the method may include: collecting current network resource status information of the network to which the third controller belongs The current network resource status information of the network to which the plurality of fourth controllers are reported by the third controller is used as the second network resource status information; and the current computing resource status information of the coordinated computing unit to which the third controller belongs is collected and multiple The current computing resource status information of the coordinated computing unit to which the plurality of fourth controllers are reported by the third controller is used as the second computing resource status information; receiving the user request, and the second network resource status information and the second computing resource status information satisfy the pre- When the condition is set, the second request is generated, where the second request includes, but is not limited to, calculating the uninstallation information identifier; generating, according to the calculated uninstallation information identifier and the preset configuration table in the second request, the current second network resource status information and the first Calculating resource state information to generate second control information corresponding to the scene data, wherein, the second The information is used to control the computing offload of the plurality of second controllers of the same level marked by the controller identifier and/or the plurality of third controllers of the lower level; the second control information is distributed to the prediction information of the node The controller in the network identifies the plurality of second controllers of the same level and/or the plurality of third controllers of the lower level, so that the marked controller calculates the pair according to the second control information and the calculated offload control mode. Uninstall for control. And acquiring, according to the computing resource offloading information identifier in the second request, the corresponding computing offloading service information, generating the computing information related to the unloading based on the historical data of calculating the uninstalled service information, and transmitting the historical information of the node requested by the user.

The first type of control mode further includes: a hybrid control mode, and when the control mode is the hybrid control mode, generating, according to the calculation of the uninstallation information identifier and the preset configuration table in the second request, the current second network resource state information and the first The second control information corresponding to the calculation of the resource state information generation scenario data includes: receiving the first control information distributed by the first controller, and receiving the second control information distributed by the plurality of second controllers of the same level.

In the embodiment of the present invention, the first type of control mode further includes: a fully distributed control mode, and when the control mode is the fully distributed control mode, collecting current network resource state information of the network to which the third controller belongs The current network resource status information of the network to which the plurality of fourth controllers are reported by the third controller is used as the second network resource status letter. The information includes: when the current control mode is the fully distributed control mode, collecting current network resource state information of the network to which the second controller belongs as the second network resource state information, and collecting the current collaborative computing unit to which the third controller belongs The computing resource state information and the current computing resource state information of the coordinated computing unit to which the plurality of fourth controllers are reported by the plurality of third controllers as the second computing resource state information, including: the full control mode in the current control mode In the mode, collecting the computing resource state information of the collaborative computing unit to which the second controller belongs as the second computing resource state information; receiving the user request, and satisfying the preset condition in the second network resource state information and the second computing resource state information And generating a second request, including: receiving a user request, and generating, when the second network resource state information and the second computing resource state information meet the preset condition, generating a network for the second controller to belong to and/or the associated collaborative computing The calculation in the unit unloads the third request for control.

S143: Receive second control information distributed by the second controller, generate a plurality of third controllers and/or subordinates of the same level according to the controller identifier in the second control information, calculate the uninstallation information identifier, and calculate the uninstall control manner. The calculation of the plurality of fourth controllers unloads the third control information that is controlled, and distributes the third control information to the corresponding third controller and/or fourth controller.

In the embodiment of the present invention, current network resource state information of multiple fourth controllers and/or multiple third controllers of the same level may be collected as the third network resource state information according to the current control mode. And reporting the third network resource status information to the second controller, and collecting current computing resources of the plurality of fourth controllers and/or the plurality of third controllers of the same level to the coordinated computing unit of the same level according to the current control mode. The status information is used as the third computing resource status information, and the third computing resource status information is reported to the second controller, according to the receiving user request, and when the third network resource status information and the third computing resource status information meet the preset condition. Generating a fourth request, wherein the fourth request includes a user request and/or a request for controlling calculation offloading of the plurality of third controllers of the same level and/or the plurality of fourth controllers of the lower level, according to the current Control mode receives or does not receive the second control information distributed by the second controller, and according to the third network resource status information and the third computing resource The state information generates third control information for controlling calculation offloading of the plurality of third controllers of the same level and/or the plurality of fourth controllers of the lower level, and distributing the third control information to the plurality of the same level The three controllers and/or the plurality of fourth controllers of the lower stage are configured to cause the marked controller to control the calculation offload according to the third control information and the calculated offload control mode.

S144: Receive third control information, generate a plurality of fourth controllers and/or subordinates of the same level according to the controller identifier, the calculation of the uninstall control mode, and the controlled calculation of the uninstallation information identifier in the third control information. The calculation of the node-level controller offloads the fourth control information for control, and distributes the fourth control information to the corresponding fourth controller and/or node-level controller.

In the embodiment of the present invention, the current network resource state information of the network to which the multiple fourth controllers of the same level belong and/or the current network resource state of the network to which the plurality of node-level controllers of the lower level belong may be collected according to the current control mode. Letter The information is used as the fourth network resource status information, and the fourth network resource status information is reported to the third controller; and the current computing resource status of the coordinated computing unit to which the plurality of fourth controllers of the same level belong is collected according to the current control mode. The information and/or the current computing resource state information of the coordinated computing unit to which the plurality of node-level controllers of the lower level belong are used as the fourth computing resource state information, and the fourth computing resource state information is reported to the third controller; according to the current control mode Receiving or not receiving the user request, and generating a fifth request when the fourth network resource state information and the fourth computing resource state information meet the preset condition, wherein the fifth request includes the user request and/or is used for the peer Computing a plurality of fourth controllers and/or a plurality of node level controllers to perform a request for control; receiving or not receiving third control information distributed by the third controller according to the current control mode, and according to the fourth network resource status Information and fourth computing resource state information are generated for controlling multiple fourth controllers and/or multiple node levels of the same level The calculation of the device unloads the fourth control information for control, and distributes the fourth control information to the controller marked by the controller identifier to cause the marked controller to control the calculation of the uninstallation.

S145: Acquire, according to the fourth control information, the calculated uninstallation information corresponding to the calculated uninstallation information identifier, and in the collaborative computing unit corresponding to the node level controller, calculate the offload control mode to the node level controller according to the fourth control information. The computational unloading of the corresponding collaborative computing unit is controlled.

S146: Generate a combination of different virtual controller clusters based on at least two preset controllers, and control the mobile computing offload cooperative control system to switch among different combinations of virtual controller clusters, and the mobile computing offload cooperative control system is according to the first A request is made to control the computational offloading of controllers within different virtual controller cluster combinations, wherein the preset controllers included in the combination of different virtual controller clusters are different.

In an embodiment of the present invention, a combination of different virtual controller clusters may be generated based on at least two preset controllers when the control mode is the second type of control mode.

Acquiring a controller corresponding to the controller identifier that receives the first request, determining whether the computing resource corresponding to the unloading information identifier in the first request is supported in the computing resource of the corresponding controller, and calculating in the support and the first request When the computing resource corresponding to the information identifier is uninstalled, the corresponding controller is determined as the target controller, the target controller is configured as the cluster head controller of the virtual controller cluster, and the controller associated with the target controller is configured as virtual Member controller of the controller cluster.

By generating a virtual controller cluster when the control mode is the second type of control mode, the mobile computing offload collaborative optimization control based on a specific controller is completed, and the flexibility of the mobile computing offload collaborative control method is improved.

It should be noted that the description of the mobile computing offload cooperative control system in the foregoing embodiments of the present invention is also applicable to the mobile computing offload cooperative control method of the embodiment, and the implementation principle is similar, and details are not described herein again. .

In this embodiment, when receiving the first request, the first controller generates corresponding control information according to the calculated uninstallation information identifier and the control mode in the first request, and distributes the control information to the second controller step by step. The third controller, the fourth controller, and the node level controller are capable of performing software-defined mobile computing offload collaborative optimization control, flexibly supporting user-centric or computing-based and/or network resource optimization-centric differences The mobile computing offloads the collaborative optimization goal and improves the scalability and flexibility of the mobile computing offload collaborative control method.

It should be noted that in the description of the present invention, the terms "first", "second" and the like are used for descriptive purposes only, and are not to be construed as indicating or implying relative importance. Further, in the description of the present invention, the meaning of "a plurality" is two or more unless otherwise specified.

Any process or method description in the flowcharts or otherwise described herein may be understood to represent sub-modules, segments or code of code comprising one or more executable instructions for implementing the steps of a particular logical function or process. The scope of the preferred embodiments of the invention includes additional implementations, in which the functions may be performed in a substantially simultaneous manner or in an opposite order depending on the functions involved, in the order shown or discussed. It should be understood by those skilled in the art to which the embodiments of the present invention pertain.

It should be understood that portions of the invention may be implemented in hardware, software, firmware or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one or combination of the following techniques well known in the art: having logic gates for implementing logic functions on data signals. Discrete logic circuits, application specific integrated circuits with suitable combinational logic gates, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

One of ordinary skill in the art can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by a program to instruct related hardware, and the program can be stored in a computer readable storage medium. When executed, one or a combination of the steps of the method embodiments is included.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing sub-module, or each unit may exist physically separately, or two or more units may be integrated into one sub-module. The above integrated sub-module can be implemented in the form of hardware or in the form of a software function sub-module. The integrated sub-module, if implemented in the form of a software function sub-module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.

The above mentioned storage medium may be a read only memory, a magnetic disk or an optical disk or the like.

In the description of the present specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like means a specific feature described in connection with the embodiment or example. A structure, material or feature is included in at least one embodiment or example of the invention. In this specification, the schematic representation of the above terms is not necessarily Refers to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples.

Although the embodiments of the present invention have been shown and described, it is understood that the above-described embodiments are illustrative and are not to be construed as limiting the scope of the invention. The embodiments are subject to variations, modifications, substitutions and variations.

Claims

A mobile computing offload cooperative control system, wherein the mobile computing offload cooperative control system comprises at least two preset controllers and a virtual controller cluster generating module, wherein the preset controller is as follows One:

The first controller is configured to: collect the current first network resource state information and the first computing resource state information reported by the multiple second controllers when the first request is generated, and obtain the current control mode from the preset configuration table, And generating first control information corresponding to the current first network resource state information and the first computing resource state information generated scenario data according to the calculated uninstallation information identifier and the control mode in the first request, and The first control information is distributed to the corresponding controller, where the first control information includes, but is not limited to, a controller identifier, a calculation uninstallation control mode, and a controlled calculation uninstallation information identifier;

a plurality of second controllers, configured to receive first control information distributed by the first controller, and according to the controller identifier in the first control information, the calculating offload control mode, and the controlled Calculating the offload information identifier to generate second control information for controlling the calculation offload of the plurality of second controllers of the same level and/or the plurality of third controllers of the lower level, and distributing the second control information to the corresponding a second controller and/or a third controller;

a plurality of third controllers, configured to: when receiving the second control information distributed by the second controller, according to the controller identifier in the second control information, the calculating offload control And the controlled calculation offload information identifier generates third control information for controlling calculation offloading of the plurality of third controllers of the same level and/or the plurality of fourth controllers of the lower level, and the third The control information is distributed to the corresponding third controller and/or fourth controller;

a plurality of fourth controllers, configured to: according to the controller identifier in the third control information, the calculation offload control mode, and the controlled calculation uninstallation information, when the third control information is received Identifying fourth control information for controlling calculation offloading of a plurality of fourth controllers of the same level and/or a plurality of node level controllers of the lower level, and distributing the fourth control information to the corresponding fourth controller And/or node level controllers;

a plurality of node level controllers, configured to acquire, according to the fourth control information, calculation offload information corresponding to the controlled calculation offload information identifier, in a collaborative computing unit corresponding to the node level controller Controlling the unloading of the collaborative computing unit corresponding to the node level controller according to the calculating the unloading control mode in the fourth control information;

The virtual controller cluster generating module is configured to generate a combination of different virtual controller clusters based on the at least two preset controllers, and control the mobile computing offload collaborative control system in the different virtual controllers Switching in a combination of clusters, the mobile computing offload cooperative control system according to the first request to a combination of different virtual controller clusters Control offloading of the controller is controlled, wherein the preset controllers included in the combination of the different virtual controller clusters are different;

The types of the first controller, the second controller, the third controller, and the fourth controller may be a global controller, a macro base station level controller, and a micro base station level controller, respectively. And any of the micro cloud cluster head controllers.
The mobile computing offload cooperative control system according to claim 1, wherein the control mode comprises: a first type of control mode and a second type of control mode, wherein the first type of control mode is a controller topology And the mobile radio access network physically calculates the same control mode of the unloading node topology, and the second type of control mode is a control mode in which the controller topology and the mobile radio access network physically calculate the unloading node topology.
The mobile computing offload cooperative control system according to claim 2, wherein the virtual controller cluster generation module is further configured to:

When the control mode is the second type of control mode, a combination of different virtual controller clusters is generated based on the at least two preset controllers.
The mobile computing cluster unloading collaborative control system of claim 1 , wherein the virtual controller cluster generation module comprises:

Obtaining a submodule, configured to acquire a controller corresponding to the controller identifier that receives the first request;

a determining sub-module, configured to determine, by the computing resource of the corresponding controller, whether a computing resource corresponding to the unloading information identifier in the first request exists;

a determining submodule, configured to determine the corresponding controller as a target controller when there is no computing resource corresponding to the unloading information identifier in the first request;

a configuration submodule configured to configure the target controller as a virtual cluster head controller of a virtual controller cluster, and configure a controller associated with the target controller as a member controller of the virtual controller cluster .
The mobile computing offload cooperative control system of claim 1 further comprising:

a configuration module, configured to: the first controller, the plurality of second controllers, the plurality of third controllers, the plurality of fourth controllers, and the plurality of nodes The control mode of the level controller is configured, and the configured control mode is written into the preset configuration table.
The mobile computing offload cooperative control system according to claim 1, wherein the first controller comprises:

a first network resource status statistics submodule, configured to collect the plurality of second controllers, and/or the plurality of third controllers, and/or current network resources of the network to which the plurality of fourth controllers belong Status information as first network resource status information;

a first computing resource status statistics submodule, configured to collect the plurality of second controllers, and/or the plurality of third controllers, and/or the collaborative computing unit to which the plurality of fourth controllers belong Computing resource status information as first computing resource status information;

a first service proxy sub-module, configured to receive a user request, and determine, according to the user request, whether the first network resource state information and the first computing resource state information meet a preset condition, and meet the preset The first request is generated, where the first request includes: a calculation uninstallation information identifier corresponding to the user request;

a first control information generating submodule, configured to generate, according to the calculated uninstallation information identifier and the preset configuration table in the first request, corresponding to the current first network resource state information and the first computing resource state information generated scene data The first control information, wherein the first control information is used to perform calculation offloading of the controller of the controller identifier in the network to which the controlled computing offload corresponding controller identifier and/or prediction information is directed control;

a first distribution submodule, configured to distribute the first control information to the controller identifier of the controller identifier in the network corresponding to the controlled calculation offload and/or the controller identifier in the network to which the prediction information is directed, So that the marked controller controls the calculation offload according to the first control information and the offload control mode.
The mobile computing offloading cooperative control system according to claim 6, wherein the first control information generating submodule comprises:

a converting unit, configured to generate scenario data according to the calculated uninstallation information identifier and the preset configuration table in the first request, and the first network resource state information and the first computing resource state information, and convert the first request into a basis Calculation offload optimization problem for a specific target;

An algorithm selection decision unit is configured to perform an algorithm selection according to the calculating offload optimization problem;

The algorithm unit is configured to generate the first control information after selecting a preset algorithm.
The mobile computing offload cooperative control system of claim 6, wherein the first controller further comprises:

a first user mobile computing offloading information analysis submodule, configured to acquire, according to the calculated uninstallation information identifier in the first request, the calculated uninstallation information corresponding to the calculated uninstallation information identifier, and based on the calculated unloaded historical data, and Sending history information of the node requested by the user, and generating prediction information related to the calculation of the uninstallation.

a first application management submodule, configured to store and manage a computing application supported by the first controller, and invoke the application according to the first request, including a registry, an application manager, and a computing application;

a first node management submodule, configured to manage an associated general node and/or a collaborative computing unit;

The first controller control submodule is configured to control and manage control information and interaction between the first controller and a controller associated with the first controller.
The mobile computing offload cooperative control system according to claim 8, wherein when the first controller is a controller of the virtual controller cluster, the first controller control submodule is further configured to activate the The sub-modules of the first controller of the virtual controller cluster enable state.
The mobile computing offload cooperative control system according to claim 2, wherein the first type of control mode comprises: a centralized control mode, and the second controller comprises:

a second network resource status statistic sub-module, configured to collect current network resource status information of the network to which the multiple third controller belongs and the multiple third control when the current control mode is the centralized control mode The current network resource status information of the network to which the plurality of fourth controllers are reported is used as the second network resource status information;

a second computing resource status statistic sub-module, configured to collect, when the current control mode is the centralized control mode, current computing resource status information of the coordinated computing unit to which the plurality of third controllers belong, and the plurality of The current computing resource status information of the coordinated computing unit to which the plurality of fourth controllers are reported by the three controllers is used as the second computing resource status information;

a second service agent sub-module, configured to receive the user request when the current control mode is the centralized control mode, and satisfy the pre-determination in the second network resource state information and the second computing resource state information When the condition is set, the second request is generated according to the user request and the first control information, where the second request includes, but is not limited to, calculating an uninstallation information identifier;

a second control information generating submodule, configured to generate, according to the calculated uninstallation information identifier and the preset configuration table in the second request, corresponding to the current second network resource state information and the second computing resource state information generated scene data Second control information, wherein the second control information is used to control calculation offloading of the plurality of second controllers of the same level and/or the plurality of third controllers of the lower level marked by the controller identifier ;

a second distribution submodule, configured to distribute the second control information to the plurality of second controllers of the peers marked by the controller identifier and/or the plurality of third controllers of the lower level, so as to The marked controller controls the calculation offload according to the second control information and the calculated offload control mode.
The mobile computing offloading cooperative control system according to claim 10, wherein the second control information generating submodule comprises:

a converting unit, configured to receive the calculated uninstallation information identifier and the preset configuration table in the second request, and the scenario data generated by the second network resource state information and the second computing resource state information, where the second request is Convert to a specific target-based computational offload optimization problem;

An algorithm selection decision unit is configured to perform an algorithm selection according to the calculating offload optimization problem;

The algorithm unit is configured to generate the second control information after selecting a preset algorithm.
A mobile computing offload cooperative control system according to claim 10, wherein said second controller Also includes:

a second user mobile computing offloading information analysis submodule, configured to acquire, according to the calculated uninstallation information identifier in the second request, the calculated offloading service information corresponding to the calculated uninstallation information identifier, and based on the calculation, the history of the uninstalled service information Data, and history information of a node that sends the user request, generates prediction information related to the calculation offload.

a second application management sub-module, configured to store and manage a computing application supported by the second controller, and invoke the application according to the second request, including a registry, an application manager, and a computing application;

a second node management sub-module for managing the belonging general node and the collaborative computing unit;

The second controller control submodule is configured to control and manage control information and interaction between the second controller and the second controller and the associated controller.
The mobile computing offload cooperative control system according to claim 12, wherein when the second controller is a controller of the virtual controller cluster, the second controller control submodule is further configured to activate the The sub-module enable state of the second controller of the virtual controller cluster.
The mobile computing offload cooperative control system according to claim 2 or 10, wherein the first type of control mode further comprises: a hybrid control mode, when the control mode is the hybrid control mode,

The second control information generating sub-module is further configured to: receive first control information distributed by the first controller, and receive second control information that is distributed by multiple second controllers of the same level.
The mobile computing offload cooperative control system according to claim 2 or 10, wherein the first type of control mode further comprises: a fully distributed control mode, when the control mode is the fully distributed control mode ,

The second network resource status statistics sub-module is further configured to: when the current control mode is the full distributed control mode, collect current network resource status information of the network to which the second controller belongs as the second network resource status. information;

The second computing resource status statistics sub-module is further configured to: when the current control mode is the full distributed control mode, collect computing resource status information of the collaborative computing unit to which the second controller belongs as the second computing resource. status information;

The second service proxy sub-module is further configured to receive the user request, and generate, when the second network resource state information and the second computing resource state information meet a preset condition, A third request in the network to which the controller belongs and/or in the associated collaborative computing unit to calculate the offload control.
The mobile computing offload cooperative control system according to claim 1, wherein the third controller comprises:

a third network resource status statistic sub-module, configured to collect current network resource status information of the plurality of fourth controllers and/or a plurality of third controllers of the same level as the third according to the current control mode Network resource status letter And reporting the third network resource status information to the second controller;

a third computing resource status statistic sub-module, configured to collect, according to a current control mode, current computing resource status information of the plurality of fourth controllers and/or a plurality of third controllers of the same level to which the third computing controller belongs Calculating resource status information, and reporting the third computing resource status information to the second controller;

a third service proxy submodule, configured to generate a fourth request according to receiving the user request, and when the third network resource state information and the third computing resource state information meet a preset condition, where The fourth request includes a user request and/or a request to control calculation offloading of a plurality of third controllers of the same level and/or a plurality of fourth controllers of the lower level;

a third control information generating submodule, configured to receive or not receive the second control information that is distributed by the second controller according to the current control mode, and according to the third network resource status information and the The third computing resource state information generates third control information for controlling calculation offloading of the plurality of third controllers of the same level and/or the plurality of fourth controllers of the lower level;

a third distribution submodule, configured to distribute the third control information to the plurality of third controllers of the same level and/or the plurality of fourth controllers of the lower level, so that the labeled controller The calculation offload is controlled according to the third control information and the calculation offload control mode.
The mobile computing offloading cooperative control system according to claim 16, wherein the third control information generating submodule comprises:

a converting unit, configured to convert, according to the calculated uninstallation information identifier and the preset configuration table in the third request, and the scenario data generated by the third network resource state information and the third computing resource state information, to convert the third request into A computational offload optimization problem based on a specific target;

An algorithm selection decision unit is configured to perform an algorithm selection according to the calculating offload optimization problem;

And an algorithm unit, configured to generate the third control information after selecting a preset algorithm.
The mobile computing offload cooperative control system according to claim 1, wherein the fourth controller comprises:

a fourth network resource status statistics sub-module, configured to collect current network resource status information of a network of a plurality of fourth controllers of the same level according to the current control mode, and/or a current network of the network to which the plurality of node-level controllers of the lower level belong The resource status information is used as the fourth network resource status information, and the fourth network resource status information is reported to the third controller;

a fourth computing resource state statistics sub-module, configured to collect, according to the current control mode, current computing resource state information of the coordinated computing unit to which the multiple fourth controllers of the same level belong and/or coordinated computing of multiple node-level controllers of the lower level The current computing resource status information of the unit is used as the fourth computing resource status information, and the fourth computing resource status information is The information is reported to the third controller;

a fourth service proxy submodule, configured to receive or not receive the user request according to a current control mode, and when the fourth network resource state information and the fourth computing resource state information meet the preset condition, Generating a fifth request, wherein the fifth request includes a user request and/or a request to control computational offloading of a plurality of fourth controllers and/or a plurality of node level controllers of the same level;

a fourth control information generating submodule, configured to receive or not receive third control information that is distributed by the third controller according to the current control mode, and according to the fourth network resource state information and the fourth computing resource state The information generates fourth control information for controlling computational offloading of the plurality of fourth controllers and/or the plurality of node level controllers of the same level;

And a fourth distribution submodule, configured to distribute the fourth control information to the controller marked by the controller identifier, so that the marked controller controls the calculation uninstallation.
The mobile computing offloading cooperative control system according to claim 18, wherein the fourth control information generating submodule comprises:

a converting unit, configured to generate scenario data according to the calculated uninstallation information identifier and the preset configuration table in the fourth request, and the fourth network resource state information and the fourth computing resource state information, and convert the fourth request service request Unload optimization problems for specific target-based calculations;

An algorithm selection decision unit is configured to perform an algorithm selection according to the calculating offload optimization problem;

And an algorithm unit, configured to generate the fourth control information after selecting a preset algorithm.
The mobile computing offload cooperative control system according to claim 1, wherein the first controller, the plurality of second controllers, the plurality of third controllers, the plurality of fourth controllers, and the plurality of node level controllers The collaborative computing unit is configured to selectively provide the computing resource to which the collaborative computing unit belongs and the computing offloading service to which the collaborative computing unit belongs.
A mobile computing offload collaborative control method, comprising:

When the first request is generated, the current first network resource status information and the first computing resource status information reported by the multiple second controllers are collected, and the current control mode is obtained from the preset configuration table, and according to the first request. Computing the offloading information identifier, the control mode generating first control information corresponding to the current first network resource state information and the first computing resource state information generating scene data, and distributing the first control information to Corresponding controller, wherein the first control information includes, but is not limited to: a controller identifier, a calculation uninstallation control mode, and the controlled calculation uninstallation information identifier;

Receiving first control information distributed by the first controller, and generating a pair of peers according to the controller identifier in the first control information, the calculation offload controller, and the controlled calculated offload information identifier Multiple second controls And/or calculating, by the plurality of third controllers of the lower stage, offloading the second control information for controlling, and distributing the second control information to the corresponding second controller and/or the third controller;

Receiving second control information distributed by the second controller, generating, according to the controller identifier, the calculating and uninstalling control mode, and the controlled calculated uninstallation information identifier in the second control information Calculating, by the plurality of third controllers and/or the fourth controller of the lower level, the third control information that is controlled, and distributing the third control information to the corresponding third controller and/or the fourth controller;

Receiving the third control information, generating a plurality of fourth controllers of the same level according to the controller identifier, the calculated offload control mode, and the controlled calculated offload information identifier in the third control information And/or calculating, by the plurality of node level controllers of the lower level, the fourth control information for controlling, and distributing the fourth control information to the corresponding fourth controller and/or the node level controller;

Obtaining, according to the fourth control information, the calculated uninstallation information corresponding to the controlled calculation offload information identifier, in the collaborative computing unit corresponding to the node level controller, calculating according to the fourth control information The unloading control mode controls the calculation and unloading of the collaborative computing unit corresponding to the node level controller;

Generating a combination of different virtual controller clusters based on the at least two preset controllers, and controlling the mobile computing offload cooperative control system to switch among combinations of the different virtual controller clusters, the mobile computing offloading The collaborative control system controls the computing offloading of the controllers in the different virtual controller cluster combinations according to the first request, wherein the preset controllers included in the combination of the different virtual controller clusters are different;

The types of the first controller, the second controller, the third controller, and the fourth controller may be a global controller, a macro base station level controller, and a micro base station level controller, respectively. And any of the micro cloud cluster head controllers.
The mobile computing offload cooperative control method according to claim 21, wherein the control mode comprises: a first type of control mode and a second type of control mode, wherein the first type of control mode is a controller topology And the mobile radio access network physically calculates the same control mode of the unloading node topology, and the second type of control mode is a control mode in which the controller topology and the mobile radio access network physically calculate the unloading node topology.
The mobile computing offload cooperative control method according to claim 22, wherein the generating a combination of different virtual controller clusters based on the at least two preset controllers comprises:

When the control mode is the second type of control mode, a combination of different virtual controller clusters is generated based on the at least two preset controllers.
The mobile computing offload cooperative control method according to claim 21, wherein a combination of different virtual controller clusters is generated based on the at least two preset controllers, and the mobile computing offload cooperative control system is controlled Switching in a combination of the different virtual controller clusters, the mobile computing offloading collaborative control system according to the first Requesting control over the computational offload of controllers within different virtual controller cluster combinations, including:

Obtaining a controller corresponding to the controller identifier that receives the first request;

Determining, by the computing resource of the corresponding controller, whether a computing resource corresponding to the unloading information identifier in the first request exists;

When there is no computing resource corresponding to the calculating the uninstallation information identifier in the first request, determining the corresponding controller as the target controller;

The target controller is configured as a virtual cluster head controller of a virtual controller cluster, and a controller associated with the target controller is configured as a member controller of the virtual controller cluster.
The mobile computing offload cooperative control method according to claim 21, further comprising:

Configuring a control mode of the first controller, the plurality of second controllers, the plurality of third controllers, the plurality of fourth controllers, and the plurality of node level controllers, And writing the configured control mode to the preset configuration table.
The mobile computing offload cooperative control method according to claim 21, wherein when the first request is generated, the current first network resource status information and the first computing resource status reported by the plurality of second controllers are collected. And acquiring the current control mode from the preset configuration table, and generating, according to the calculated uninstallation information identifier and the control mode in the first request, the current first network resource state information and the first computing resource state information. Generating the first control information corresponding to the scene data, and distributing the first control information to the corresponding controller, including:

Collecting, by the plurality of second controllers, and/or the plurality of third controllers, and/or current network resource state information of the network to which the plurality of fourth controllers belong, as the first network resource state information;

Collecting, by the plurality of second controllers, and/or the plurality of third controllers, and/or the computing resource state information of the coordinated computing unit to which the plurality of fourth controllers belong, as the first computing resource state information;

Receiving a user request, and determining, according to the user request, whether the first network resource state information and the first computing resource state information meet a preset condition, and when the preset condition is met, generating the first request The first request includes at least calculating an uninstallation information identifier;

And calculating, by using the calculated uninstallation information in the first request, the uninstallation information identifier and the preset configuration table, to generate first control information corresponding to the current first network resource state information and the first computing resource state information generated scenario data, where And the first control information is used to control, for the calculation offloading, the controller uninstallation corresponding to the controller identifier and/or the controller uninstallation of the controller in the network pointed to by the prediction information;

Distributing the first control information to the controlled controller offload corresponding controller identifier and/or the controller identifier in the network pointed to by the prediction information to mark the controller, so that the marked control The controller performs control offloading according to the first control information and the calculated offload control mode.
The mobile computing offload cooperative control method according to claim 26, wherein when the first request is generated, the current first network resource status information and the first computing resource status reported by the plurality of second controllers are collected. And acquiring the current control mode from the preset configuration table, and generating, according to the calculated uninstallation information identifier and the control mode in the first request, the current first network resource state information and the first computing resource state information. And generating the first control information corresponding to the scene data, and distributing the first control information to the corresponding controller, further includes:

And acquiring, according to the calculated uninstallation information identifier in the first request, the calculated offloading service information corresponding to the calculated uninstallation information identifier, and generating, according to the historical data of the unloading calculation, and the historical information of the node requested by the user, generating The calculating unloads relevant prediction information.
The mobile computing offload cooperative control method according to claim 22, wherein the first type of control mode comprises: a centralized control mode, the receiving first control information distributed by the first controller, and according to The controller identifier in the first control information, the controlled calculated offload information identifier, and the calculated offload control manner generate a plurality of second controllers of the same level and/or a plurality of thirds of the lower level Computation of the controller to offload the second control information for control, and distribute the second control information to the corresponding second controller and/or the third controller, including:

Collecting current network resource status information of the network to which the plurality of third controllers belongs and current network resource status information of the network to which the plurality of fourth controllers are reported by the plurality of third controllers as the second network resource status information;

Collecting current computing resource state information of the coordinated computing unit to which the plurality of third controllers belongs and computing resource state information of the collaborative computing unit to which the plurality of fourth controllers are reported by the plurality of third controllers as the second Calculate resource status information;

Receiving the user request, and generating a second request according to the user request and the first control information, when the second network resource state information and the second computing resource state information meet a preset condition, where The second request includes, but is not limited to, calculating an uninstallation information identifier;

Generating second control information corresponding to the current second network resource state information and the second computing resource state information generating scenario data according to the calculated uninstallation information identifier and the preset configuration table in the second request, where The second control information is used to control calculation offloading of the plurality of second controllers of the same level marked by the controller identifier and/or the plurality of third controllers of the lower level;

Distributing the second control information to a plurality of second controllers of the same level and/or a plurality of third controllers of the lower level marked by the controller in the network pointed to by the prediction information of the node, so that the The marked controller controls the calculation offload based on the second control information and the calculated offload control mode.
The mobile computing offload cooperative control method according to claim 28, wherein when the first request is generated, the current first network resource status information and the first computing resource status reported by the plurality of second controllers are collected. Information, obtaining a current control mode from a preset configuration table, and identifying and using the computing resource information according to the first request And generating, by the control mode, the first control information corresponding to the current first network resource state information and the first computing resource state information generating scenario data, and distributing the first control information to the corresponding controller, further comprising:

Acquiring the calculated offloading service information corresponding to the computing resource information identifier according to the computing resource information identifier in the second request, and calculating historical data of the uninstalling service information based on the calculating, and sending the historical information of the node requested by the user And generating the prediction information related to the calculation offload.
The mobile computing offload cooperative control method according to claim 22 or 28, wherein the first type of control mode further comprises: a hybrid control mode, when the control mode is the hybrid control mode,

And generating the second control information corresponding to the current second network resource state information and the second computing resource state information generated scenario data according to the calculating the uninstallation information identifier and the preset configuration table in the second request, including: Receiving first control information distributed by the first controller, and receiving second control information distributed by multiple second controllers of the same level.
The mobile computing offload cooperative control method according to claim 22 or 28, wherein the first type of control mode further comprises: a fully distributed control mode, when the control mode is the fully distributed control mode ,

Collecting current network resource state information of the network to which the plurality of third controllers belongs and current network resource state information of the network to which the plurality of fourth controllers are reported by the plurality of third controllers as the second network The resource status information includes: collecting current network resource status information of the network to which the second controller belongs as the second network resource status information, when the current control mode is the full distributed control mode;

Collecting current computing resource state information of the collaborative computing unit to which the plurality of third controllers belongs and current computing resource state information of the collaborative computing unit to which the plurality of fourth controllers are reported by the plurality of third controllers The second computing resource state information includes: when the current control mode is the full distributed control mode, collecting computing resource state information of the collaborative computing unit to which the second controller belongs as the second computing resource state information;

Receiving the user request, and generating a second request according to the user request and the first control information, when the second network resource state information and the second computing resource state information meet a preset condition, The method includes: receiving the user request, and generating, when the second network resource state information and the second computing resource state information meet a preset condition, a network and/or a collaboration for the second controller to belong to The calculation in the calculation unit unloads the third request for control.
The mobile computing offload cooperative control method according to claim 21, wherein the receiving the second control information distributed by the second controller is based on the controller identifier and the location in the second control information Calculating the calculated unloading information identifier and the calculating the offloading control manner to generate third control information for controlling calculation offloading of the plurality of third controllers of the same level and/or the plurality of fourth controllers of the lower level, and The third control information is distributed to the corresponding third controller and/or the fourth controller, including:

Acquiring the plurality of fourth controllers and/or the network of the plurality of third controllers of the same level according to the current control mode The current network resource status information is used as the third network resource status information, and the third network resource status information is reported to the second controller;

Collecting current computing resource state information of the plurality of fourth controllers and/or the plurality of third controllers of the same level to the third computing controller as the third computing resource state information according to the current control mode, and collecting the third Calculating resource status information is reported to the second controller;

And generating a fourth request according to receiving the user request, and when the third network resource state information and the third computing resource state information meet a preset condition, where the fourth request includes a user request and/or a request for controlling computational offloading of a plurality of third controllers of the same level and/or a plurality of fourth controllers of the lower level;

Receiving or not receiving the second control information distributed by the second controller according to the current control mode, and generating, according to the third network resource state information and the third computing resource state information, Third control information of a plurality of third controllers of the stage and/or a plurality of fourth controllers of the lower level are controlled to be unloaded for control;

Distributing the third control information to the plurality of third controllers of the same level and/or the plurality of fourth controllers of the lower level, so that the marked controller is based on the third control information and The calculating the offload control mode controls the calculation offload.
The mobile computing offload cooperative control method according to claim 21, wherein the receiving the third control information is based on the controller identifier in the third control information, the calculating offload control mode, and The controlled calculation offload information identifier generates fourth control information for controlling calculation offloading of a plurality of fourth controllers of the same level and/or a plurality of node level controllers of the lower level, and the The fourth control information is distributed to the corresponding fourth controller and/or node level controller, including:

Collecting, according to the current control mode, the current network resource state information of the network to which the multiple fourth controllers of the same level belong and/or the current network resource state information of the network to which the plurality of node-level controllers of the lower level belong, as the fourth network resource state information, And reporting the fourth network resource status information to the third controller;

Collecting, according to the current control mode, current computing resource state information of the coordinated computing unit to which the plurality of fourth controllers of the same level belong and/or current computing resource state information of the collaborative computing unit to which the plurality of node-level controllers of the lower level belong to the fourth computing Resource status information, and reporting the fourth computing resource status information to the third controller;

Receiving or not receiving the user request according to a current control mode, and generating a fifth request when the fourth network resource state information and the fourth computing resource state information satisfy the preset condition, where The fifth request includes a user request and/or a request to control computational offloading of the plurality of fourth controllers of the peer and/or the collaborative computing unit of the plurality of node level controllers;

Receiving or not receiving the third control information distributed by the third controller according to the current control mode, and generating, according to the fourth network resource state information and the fourth computing resource state information, multiple pairs for the same level Fourth controller And/or calculating, by the computing unit of the plurality of node level controllers, the fourth control information for controlling the unloading;

Distributing the fourth control information to the controller marked by the controller identification to cause the marked controller to control the computational offload.