CN114780201A

CN114780201A - Resource adjusting method and device, electronic equipment and storage medium

Info

Publication number: CN114780201A
Application number: CN202210307687.8A
Authority: CN
Inventors: 陈京来; 赵增; 刘柏; 李仁杰; 范长杰; 胡志鹏
Original assignee: Netease Hangzhou Network Co Ltd
Current assignee: Netease Hangzhou Network Co Ltd
Priority date: 2022-03-25
Filing date: 2022-03-25
Publication date: 2022-07-22

Abstract

The present disclosure provides a resource adjusting method, device, electronic device and storage medium, the method comprising: determining the target resource occupation amount of a target Pod corresponding to the target offline task to be acquired at a target moment and the historical resource occupation amount at a historical moment, and calculating a resource increase value of the target resource occupation amount relative to the historical resource occupation amount; and in response to determining that the resource added value does not exceed the available resource amount of the offline task of the target cluster corresponding to the target node corresponding to the target Pod, and in response to determining that the resource added value does not exceed the available resource amount of the offline task of the target node, adjusting the resource occupancy of the target Pod at the target time to be the target resource occupancy. According to the resource requirements of the offline task at different moments, the resources allocated to the Pod corresponding to the task are dynamically adjusted, and the utilization rate of the resources can be improved.

Description

Resource adjusting method, device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of electronic digital data processing technologies, and in particular, to a resource adjustment method and apparatus, an electronic device, and a storage medium.

Background

This section is intended to provide a background or context to the embodiments of the disclosure recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.

Kubernets is an open source system for automatically deploying, extending and managing containerized applications that combines the containers that make up the application into a logical unit to facilitate management and service discovery.

Kubernets organizes containers into a cluster, and adopts a cluster architecture of one Master and multiple slaves, namely, one Master Node (Master) is used for managing a plurality of sub-nodes (nodes), and Pod (combination of one or more containers) is used as a basic unit to be distributed to the appropriate sub-nodes. When processing a task, kubernets select a child node appropriate for the task, allocate resources on the child node and assign a Pod to complete the task.

However, the available resources of the Kubernetes cluster are dynamically changed, and the resource demand of the task is also dynamically changed, in the prior art, once the resources are allocated, the subsequent adjustment is not performed, which may cause resource waste or low efficiency to a certain extent.

Disclosure of Invention

In view of the above, an object of the present disclosure is to provide a resource adjustment method, apparatus, electronic device and storage medium.

Based on the above purpose, an exemplary embodiment of the present disclosure provides a resource adjustment method, which is implemented based on a kubernets system, and the method includes:

determining target resource occupation amount to be acquired by a target Pod corresponding to a target offline task at a target moment and historical resource occupation amount at a historical moment, and calculating a resource increase value of the target resource occupation amount relative to the historical resource occupation amount;

acquiring the available resource quantity of the offline task of the target cluster corresponding to the target node corresponding to the target Pod, responding to the fact that the resource increment value does not exceed the available resource quantity of the offline task of the target cluster, acquiring the available resource quantity of the offline task of the target node, and judging whether the resource increment value exceeds the available resource quantity of the offline task of the target node or not;

and in response to determining that the resource increase value does not exceed the amount of resources available for the offline task of the target node, adjusting the resource occupancy of the target Pod at the target time to be the target resource occupancy.

In some exemplary embodiments, after said calculating a resource increase value of said target resource occupancy relative to said historical resource occupancy, further comprises:

in response to determining that the resource increment value exceeds a resource increment value threshold, determining whether the target Pod is in a resource adjustment cooling phase;

before the obtaining of the amount of the available resources of the offline task of the target cluster corresponding to the target node corresponding to the target Pod, the method further includes:

determining that the target Pod is not in the resource adjustment cooling phase.

In some exemplary embodiments, the determining whether the target Pod is in a resource adjustment cooling phase includes:

acquiring the adjustment time of the last resource adjustment of the target Pod;

determining that the target Pod is not in the resource adjustment cooling phase in response to determining that the time difference between the target time and the adjustment time exceeds a preset time difference threshold.

In some exemplary embodiments, the determining the target resource occupancy to be obtained by the target Pod corresponding to the target offline task at the target time includes:

acquiring the resource quantity demand of the target Pod at the target moment;

and obtaining the target resource occupation amount according to the resource amount demand and the preset expected utilization rate of the Pod resources.

In some exemplary embodiments, after determining the target resource occupation amount to be obtained by the target Pod corresponding to the target offline task at the target time, the method further includes:

in response to the fact that the target resource occupancy amount exceeds a preset Pod resource occupancy amount upper limit, taking the value of the Pod resource occupancy amount upper limit as the value of the target resource occupancy amount.

In some exemplary embodiments, the obtaining the amount of resources available for the offline task of the target cluster corresponding to the target node corresponding to the target Pod includes:

acquiring the resource quantity of a cluster offline task pool of the target cluster;

acquiring the resource occupation amount of the cluster offline task of the target cluster;

and obtaining the available resource quantity of the off-line task of the target cluster according to the resource quantity of the cluster off-line task resource pool and the resource quantity occupied by the cluster off-line task.

In some exemplary embodiments, the obtaining the cluster offline task resource pool resource amount of the target cluster includes:

acquiring the total cluster resource amount of the target cluster and the resource amount occupied by the cluster online service;

acquiring a preset expected utilization rate of cluster resources of the target cluster and a preset reserved resource amount of cluster online service;

and obtaining the resource quantity of the cluster offline task resource pool according to the total cluster resource quantity, the expected utilization rate of the cluster resources, the resource quantity occupied by the cluster online service and the reserved resource quantity of the cluster online service.

In some exemplary embodiments, the obtaining the amount of resources available for the offline task of the target node includes:

acquiring the resource quantity of a node offline task resource pool of the target node;

acquiring the resource amount occupied by the node offline task of the target node;

and obtaining the available resource quantity of the off-line task of the target node according to the resource quantity of the node off-line task resource pool and the resource quantity occupied by the node off-line task.

In some exemplary embodiments, the obtaining the resource amount of the node offline task resource pool of the target node includes:

acquiring the total node resource amount of the target node and the resource amount occupied by the node online service;

acquiring a preset node resource expected utilization rate of the target node and a preset node online service reserved resource amount;

and obtaining the resource quantity of the node offline task resource pool according to the total node resource quantity, the expected utilization rate of the node resource, the occupied resource quantity of the node online service and the reserved resource quantity of the node online service.

In some exemplary embodiments, the resources include at least one of:

CPU, memory, disk IO and network IO.

Based on the same inventive concept, the exemplary embodiments of the present disclosure further provide a resource adjusting apparatus, which is implemented based on a kubernets system, and the apparatus includes:

the resource increment value determining module is configured to determine the target resource occupation amount of a target Pod corresponding to the target offline task to be acquired at a target moment and the historical resource occupation amount at a historical moment, and calculate a resource increment value of the target resource occupation amount relative to the historical resource occupation amount;

a resource first determining module, configured to obtain an offline task available resource amount of a target cluster corresponding to a target node corresponding to the target Pod, obtain the offline task available resource amount of the target node in response to determining that the resource added value does not exceed the offline task available resource amount of the target cluster, and determine whether the resource added value exceeds the offline task available resource amount of the target node;

a resource occupation adjusting module configured to adjust the resource occupation amount of the target Pod at the target time to the target resource occupation amount in response to determining that the resource increase value does not exceed the offline task available resource amount of the target node.

Based on the same inventive concept, the exemplary embodiments of the present disclosure also provide an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the method as described above is implemented.

Based on the same inventive concept, the disclosed exemplary embodiments also provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method as described in any one of the above.

As can be seen from the foregoing, in the resource adjustment method, apparatus, electronic device and storage medium provided in the embodiments of the present disclosure, the method includes: determining the target resource occupation amount of a target Pod corresponding to the target offline task to be acquired at a target moment and the historical resource occupation amount at a historical moment, and calculating a resource increase value of the target resource occupation amount relative to the historical resource occupation amount; and adjusting the resource occupation amount of the target Pod at the target moment to be the target resource occupation amount in response to determining that the resource increase value does not exceed the offline task available resource amount of the target cluster corresponding to the target node corresponding to the target Pod and in response to determining that the resource increase value does not exceed the offline task available resource amount of the target node. According to the resource requirements of the offline task at different moments, the resources allocated to the Pod corresponding to the task are dynamically adjusted, and the utilization rate of the resources can be improved.

Drawings

In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings needed to be used in the description of the embodiments or related technologies are briefly introduced below, and it is obvious that the drawings in the following description are only embodiments of the present disclosure, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.

Fig. 1 is a schematic flow chart of a resource adjustment method according to an embodiment of the present disclosure;

fig. 2 is another schematic flow chart of a resource adjustment method according to an embodiment of the present disclosure;

fig. 3 is another schematic flow chart of a resource adjustment method according to an embodiment of the present disclosure;

fig. 4 is a schematic structural diagram of a resource adjusting apparatus according to an embodiment of the present disclosure;

fig. 5 is another schematic structural diagram of a resource adjustment apparatus according to an embodiment of the present disclosure;

fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.

Detailed Description

To make the objects, technical solutions, and advantages of the present disclosure more apparent, the principles and spirit of the present disclosure will be described below with reference to several exemplary embodiments. It is understood that these embodiments are given solely for the purpose of enabling those skilled in the art to better understand and to practice the present disclosure, and are not intended to limit the scope of the present disclosure in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

According to an embodiment of the disclosure, a resource adjusting method, a resource adjusting device, an electronic device and a storage medium are provided.

In this document, it is to be understood that any number of elements in the figures are provided by way of illustration and not limitation, and any nomenclature is used for differentiation only and not in any limiting sense.

It is to be noted that technical terms or scientific terms used in the embodiments of the present disclosure should have a general meaning as understood by those having ordinary skill in the art to which the present disclosure belongs, unless otherwise defined. The use of "first," "second," and similar terms in the embodiments of the disclosure is not intended to indicate any order, quantity, or importance, but rather to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item preceding the word comprises the element or item listed after the word and its equivalent, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used only to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.

The principles and spirit of the present disclosure are explained in detail below with reference to several representative embodiments thereof.

In the related art, a resource allocation scheme based on kubernets has the problems of resource waste or low efficiency.

The inventors of the present disclosure found that the reason for the above-mentioned problem of resource waste or low efficiency in the related art is: the available resources of the kubernets cluster are dynamically changed, and the resource demand of the task is also dynamically changed, while in the related art, when resource allocation is performed, only the available resources of the kubernets cluster and the resource demand of the task are concerned, and once resources are allocated, whether the available resources of the kubernets cluster and the resource demand of the task are changed or not is not concerned. Under this scheme, when the available resources of the kubernets cluster change, e.g. increase, the available resources of the task increase, but it obviously loses the opportunity to improve efficiency since the resources allocated by the task do not change. When the resource demand of a task changes, for example, decreases, more resources are allocated because the resource demand when the task is allocated with resources is higher, and when the resource demand decreases, the resource is wasted.

In addition, in the application scenario of the present disclosure, kubernets mix and deploy online services (usually delay-sensitive high-priority tasks) and offline tasks (usually CPU-consuming low-priority tasks) on the same node, so as to improve the resource utilization of the node.

In a resource management scheme based on Kubernetes in the related technology, offline tasks on the same node are taken as a whole, and the size of a node offline task resource pool is uniformly managed so as to control the whole influence of the offline tasks on online services.

However, since the offline tasks share the node offline task resource pool, the offline tasks have the competition influence of mutual resources; it is easy to cause one offline task to affect all offline tasks on the node.

In order to solve the above problem, the present disclosure provides a resource adjustment scheme, which specifically includes: determining the target resource occupation amount of a target Pod corresponding to the target offline task to be acquired at a target moment and the historical resource occupation amount at a historical moment, and calculating a resource increase value of the target resource occupation amount relative to the historical resource occupation amount; acquiring the available resource quantity of the offline task of a target cluster corresponding to a target node corresponding to the target Pod, responding to the fact that the resource added value does not exceed the available resource quantity of the offline task of the target cluster, acquiring the available resource quantity of the offline task of the target node, and judging whether the resource added value exceeds the available resource quantity of the offline task of the target node or not; and in response to the fact that the resource increase value does not exceed the available resource amount of the offline task of the target node, adjusting the resource occupation amount of the target Pod at the target moment to be the target resource occupation amount. According to the resource requirements of the offline task at different moments, the resources allocated to the Pod corresponding to the task are dynamically adjusted, and the utilization rate of the resources can be improved.

Having described the general principles of the present disclosure, various non-limiting embodiments of the present disclosure are described in detail below.

Referring to fig. 1, a resource adjustment method is implemented based on a kubernets system, and the method includes the following steps:

step S110, determining the target resource occupation amount of the target Pod corresponding to the target off-line task to be obtained at the target moment and the historical resource occupation amount at the historical moment, and calculating a resource increase value of the target resource occupation amount relative to the historical resource occupation amount.

Among other things, the present disclosure is implemented based on the kubernets system. Kubernets is an open source system for automatically deploying, extending and managing containerized applications that combines the containers that make up the application into a logical unit.

In specific implementation, kubernets organize containers into a cluster, and adopt a cluster architecture of one Master and multiple slaves, that is, one Master Node (Master) is used to manage multiple child nodes (nodes), and Pod (combination of one or multiple containers) is used as a basic unit to be allocated to appropriate child nodes. When processing a task, kubernets selects a child node appropriate for the task, allocates resources on the child node and assigns a Pod to complete the task.

Wherein, the resource occupation amount refers to the amount of resources actually occupied by the Pod.

In particular, in kubernets, any task is completed based on Pod assigned to the task, and therefore, in the expression of the present disclosure, the amount of resources allocated to Pod is the amount of resources allocated to the task.

In some exemplary embodiments, the resources include at least one of:

CPU, memory, disk IO and network IO.

Wherein the resource quantity requirements of the same task at different times may be different.

In specific implementation, taking the example that the resource includes a CPU, the demands of the same task on the CPU at different stages are different, when relatively complex operations are performed, the demands on the CPU are higher, and conversely, when relatively simple operations are performed, the demands on the CPU are lower. Typically, the resource unit of a CPU is a "core," e.g., a task's demand for a CPU is 5 cores.

In the related art, after allocating resources to a Pod, the Pod will always use the resources until the task is completed, and the amount of allocated resources will not change. In the present disclosure, the amount of resources allocated to a Pod is adjusted according to the resource amount requirements of a task at different times. Therefore, the resource amount actually occupied by the Pod corresponding to the target task at the target time, i.e., the target resource occupation amount, needs to be determined first.

In some exemplary embodiments, determining a target resource occupancy amount to be acquired by a target Pod corresponding to a target offline task at a target time includes:

acquiring the resource quantity demand of the target Pod at the target moment;

And the target resource occupation amount is the resource quantity demand/Pod resource expected utilization rate.

The resource quantity demand corresponds to the resource occupation quantity, and the resource quantity demand refers to the resource quantity which needs to be occupied by the Pod theoretically.

Specifically, considering that Pod cannot utilize one hundred percent of resources, the present disclosure sets the expected utilization rate of Pod resources, and calculates the resource occupancy on the basis of the known resource quantity demand to help Pod allocate sufficient resources.

In some exemplary embodiments, after determining the target resource occupancy to be obtained by the target Pod corresponding to the target offline task at the target time, the method further includes:

and in response to the fact that the target resource occupation amount exceeds the preset Pod resource occupation amount upper limit, taking the value of the Pod resource occupation amount upper limit as the value of the target resource occupation amount.

Wherein the target resource occupation amount is min (resource demand/Pod resource expected utilization rate, Pod resource occupation amount upper limit), where min represents the minimum value.

In the method, in consideration of the fact that other tasks also need to occupy resources, the method is provided with the occupancy upper limit of the Pod resources, and for a single task, once the target occupancy of the Pod resources exceeds the preset occupancy upper limit of the Pod resources, the Pod resources are allocated to the Pod resources at most to occupy the upper limit of the Pod resources.

Heretofore, in step S110, a resource increase value, which is a change in the amount of resources to be allocated to the target Pod corresponding to the target offline task at the target time, is determined, however, since the total amount of resources of the system is limited and other offline tasks and online tasks exist in the system, in the subsequent step, it is further determined whether the system can implement the resource change.

Step S120, obtaining the available resource quantity of the offline task of the target cluster corresponding to the target node corresponding to the target Pod, responding to the fact that the resource added value does not exceed the available resource quantity of the offline task of the target cluster, obtaining the available resource quantity of the offline task of the target node, and judging whether the resource added value exceeds the available resource quantity of the offline task of the target node.

In some exemplary embodiments, obtaining the amount of resources available for the offline task of the target cluster corresponding to the target node corresponding to the target Pod includes:

acquiring the resource quantity of a cluster offline task resource pool of a target cluster;

and obtaining the available resource quantity of the off-line tasks of the target cluster according to the resource quantity of the cluster off-line task resource pool and the resource quantity occupied by the cluster off-line tasks.

The resource pool resource amount of the cluster offline task refers to the total amount of resources in the cluster that can be allocated to the offline task. The amount of resources occupied by a cluster offline task refers to the total amount of resources in the cluster that have been allocated to the offline task.

In some exemplary embodiments, obtaining the cluster offline task resource pool resource amount of the target cluster comprises:

acquiring the total cluster resource amount of a target cluster and the resource amount occupied by the online cluster service;

acquiring a preset expected utilization rate of cluster resources of a target cluster and a preset reserved resource amount of a cluster online service;

The resource amount of the cluster offline task resource pool is max (total cluster resource amount is expected utilization rate of cluster resources-resource amount occupied by cluster online service-resource amount reserved by cluster online service, 0).

The method and the device set the reserved resources aiming at the cluster online service, avoid the excessive influence of the offline tasks on the online tasks of the same cluster, and limit the creation of the offline tasks according to the real-time offline available resource quantity of the cluster.

Wherein the cluster resource expected utilization is further to: setting expected utilization rates aiming at various isolation indexes such as a CPU (Central processing Unit), an internal memory and the like, and scheduling off-line tasks when the actual utilization rate of a cluster is less than the expected utilization rate; when the actual utilization of the cluster is greater than the expected utilization, the offline task needs to be evicted.

In some exemplary embodiments, obtaining the amount of resources available for the offline task of the target node comprises:

acquiring the resource quantity of a node offline task resource pool of a target node;

acquiring the resource occupation amount of the node offline task of the target node;

The resource amount of the node offline task resource pool refers to the total amount of resources which can be allocated to the offline task on the node. The amount of resources occupied by the offline task of a node refers to the total amount of resources that have been allocated to the offline task on the node.

In some exemplary embodiments, obtaining the resource amount of the node offline task resource pool of the target node includes:

acquiring the total node resource amount of a target node and the resource amount occupied by the node online service;

acquiring a preset node resource expected utilization rate of a target node and a preset node online service reserved resource amount;

The resource amount of the node offline task resource pool is max (total amount of the node resources is expected utilization rate of the node resources-resource amount occupied by node online service-reserved resource amount of the node online service, 0). Where max represents the maximum value.

The method and the device aim at the node online service to set reserved resources, avoid excessive influence of an offline task on the online task of the same node, and limit the creation of the offline task according to the real-time offline available resource amount of the node.

Wherein the node resource expected utilization is further for: setting expected utilization rates aiming at various isolation indexes such as a CPU (Central processing Unit), an internal memory and the like, and scheduling off-line tasks when the actual utilization rate of nodes is less than the expected utilization rate; when the actual utilization rate of the node is larger than the expected utilization rate, the offline task needs to be evicted.

And step S130, responding to the fact that the resource added value does not exceed the available resource quantity of the offline task of the target node, and adjusting the resource occupation quantity of the target Pod at the target time to be the target resource occupation quantity.

And determining whether the system can meet the requirement of the resource added value of the target Pod, and if so, adjusting the resource occupation amount of the target Pod at the target moment to be the target resource occupation amount.

As can be seen from the foregoing, a resource adjustment method provided in an embodiment of the present disclosure includes: determining target resource occupation amount of a target Pod corresponding to the target offline task to be acquired at a target moment and historical resource occupation amount at a historical moment, and calculating a resource increase value of the target resource occupation amount relative to the historical resource occupation amount; and in response to determining that the resource added value does not exceed the available resource amount of the offline task of the target cluster corresponding to the target node corresponding to the target Pod, and in response to determining that the resource added value does not exceed the available resource amount of the offline task of the target node, adjusting the resource occupancy of the target Pod at the target time to be the target resource occupancy. According to the resource requirements of the offline task at different moments, the resources allocated to the Pod corresponding to the task are dynamically adjusted, and the utilization rate of the resources can be improved.

Furthermore, the method and the device perform policy configuration aiming at three dimensions of the cluster, the node and the Pod, and have unified management of a cluster level and management of a Pod granularity level. In addition, a plurality of factors such as the influence of the offline task on the online service, the influence between the offline task and the offline task, the utilization rate of node resources, the resource management at the cluster level and the like are comprehensively considered, and the utilization rate of the resources and the efficiency of resource adjustment are improved.

Referring to fig. 2, a resource adjustment method is implemented based on a kubernets system, and the method includes the following steps:

step S210, determining the target resource occupation amount to be acquired by the target Pod corresponding to the target offline task at the target moment and the historical resource occupation amount at the historical moment, and calculating the resource increase value of the target resource occupation amount relative to the historical resource occupation amount.

Step S220, in response to determining that the resource increase value exceeds the resource increase value threshold, determining whether the target Pod is in a resource adjustment cooling phase.

In some exemplary embodiments, in response to determining that the resource increase value does not exceed the preset resource increase value threshold, the resource occupancy of the target Pod at the target time is adjusted to the target resource occupancy.

The resource increment value threshold value is set, and fine adjustment of the resource is allowed, wherein the fine adjustment has small influence on the system.

In some exemplary embodiments, before obtaining the available resource amount of the offline task of the target cluster corresponding to the target node corresponding to the target Pod, the method further includes:

determining that the target Pod is not in the asset adjustment cooling phase.

In some exemplary embodiments, determining whether the target Pod is in the asset adjustment cooling phase includes:

and in response to determining that the time difference between the target time and the adjustment time exceeds a preset time difference threshold, determining that the target Pod is not in the resource adjustment cooling phase.

The resource adjusting and cooling stage is arranged, so that frequent resource adjustment is avoided, and task abnormity caused by frequent adjustment is avoided.

Step S230, obtaining the available resource amount of the offline task of the target cluster corresponding to the target node corresponding to the target Pod, obtaining the available resource amount of the offline task of the target node in response to determining that the resource increment value does not exceed the available resource amount of the offline task of the target cluster, and determining whether the resource increment value exceeds the available resource amount of the offline task of the target node.

Step S240, responding to the fact that the resource added value does not exceed the available resource quantity of the offline task of the target node, and adjusting the resource occupation quantity of the target Pod at the target moment to be the target resource occupation quantity.

As can be seen from the above, the resource adjustment method provided in the embodiment of the present disclosure further determines whether the resource increase value exceeds the resource increase value threshold, and whether the target Pod is in the resource adjustment cooling stage, so as to allow fine adjustment of the resource to a certain extent, ensure the stationarity of resource adjustment, and improve the efficiency of resource adjustment.

In some exemplary embodiments, in response to determining that there are multiple pods that need to be resource adjusted, the resource adjustment of each Pod is a proportional linear adjustment, the expected utilization rate of the resources of each Pod is as balanced as possible, and the Pod with the most scarce resources in the pods is preferentially adjusted.

In some exemplary embodiments, the indexes of the cluster, the node, and the Pod dimension are collected regularly by the collector, including a CPU, a memory, a disk IO, a network IO, and the like.

In some exemplary embodiments, resources of a cluster, node, Pod, etc. are dynamically adjusted through the isolator.

Referring to fig. 3, a resource adjustment method is implemented based on a kubernets system, and the method includes the following steps:

step S310, determining the target resource occupation amount to be acquired by the target Pod corresponding to the target offline task at the target moment and the historical resource occupation amount at the historical moment, and calculating the resource increase value of the target resource occupation amount relative to the historical resource occupation amount.

Step S320, judging whether the resource increment value exceeds the resource increment value threshold value:

in response to determining that the resource increment value does not exceed the resource increment value threshold, jumping to step S380;

in response to determining that the resource increment value exceeds the resource increment value threshold, a jump is made to step S330.

Step S330, judging whether the target Pod is in a resource adjustment cooling stage:

skipping to the beginning in response to determining that the target Pod is in the resource adjustment cooling phase; when the target Pod is jumped to the beginning, the resource occupation amount of the target Pod at the target time is not appropriate, and the time and the amount of the resources of the target Pod need to be re-determined;

in response to determining that the target Pod is not in the asset adjustment cooling phase, it jumps to step S340.

Step S340, acquiring the available resource quantity of the offline task of the target cluster corresponding to the target node corresponding to the target Pod.

Step S350, judging whether the resource added value exceeds the available resource quantity of the offline task of the target cluster:

skipping to the beginning in response to determining that the resource increase value exceeds the amount of resources available to the offline task of the target cluster;

in response to determining that the resource increment value does not exceed the amount of resources available to the offline task of the target cluster, a jump is made to step S360.

And step S360, obtaining the available resource quantity of the offline task of the target node.

Step S370, judging whether the resource added value exceeds the available resource quantity of the offline task of the target node:

skipping to the beginning in response to determining that the resource increase value exceeds the amount of resources available for the offline task at the target node;

and skipping to step S380 in response to determining that the resource increment value does not exceed the available resource amount of the offline task of the target node.

And step S380, adjusting the resource occupation amount of the target Pod at the target moment to be the target resource occupation amount.

It should be noted that the method of the embodiments of the present disclosure may be executed by a single device, such as a computer or a server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the devices may only perform one or more steps of the method of the embodiments of the present disclosure, and the devices may interact with each other to complete the method.

It should be noted that the above describes some embodiments of the disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments described above and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

Based on the same inventive concept, the present disclosure also provides a resource adjusting apparatus corresponding to the method of any of the above embodiments.

Referring to fig. 4, a resource adjusting apparatus, implemented based on the kubernets system, includes:

the resource increase value determining module 410 is configured to determine a target resource occupation amount to be obtained by the target Pod corresponding to the target offline task at the target time and a historical resource occupation amount at the historical time, and calculate a resource increase value of the target resource occupation amount relative to the historical resource occupation amount.

In some exemplary embodiments, the resources include at least one of:

CPU, memory, disk IO and network IO.

In some exemplary embodiments, the resource increment value determination module 410 is specifically configured to:

acquiring the resource quantity demand of the target Pod at the target moment;

In some exemplary embodiments, the resource increment value determining module 410 is further configured to:

The resource first determining module 420 is configured to obtain an available resource amount of an offline task of the target cluster corresponding to the target node corresponding to the target Pod, obtain the available resource amount of the offline task of the target node in response to determining that the resource added value does not exceed the available resource amount of the offline task of the target cluster, and determine whether the resource added value exceeds the available resource amount of the offline task of the target node.

In some exemplary embodiments, the resource first determining module 420 is specifically configured to:

acquiring the resource amount occupied by the cluster offline task of the target cluster;

acquiring the total cluster resource amount of a target cluster and the resource amount occupied by the cluster online service;

and obtaining the resource amount of the cluster offline task resource pool according to the total cluster resource amount, the expected utilization rate of the cluster resources, the resource amount occupied by the cluster online service and the reserved resource amount of the cluster online service.

acquiring the resource occupation amount of node offline tasks of a target node;

and obtaining the resource quantity of the node off-line task pool according to the total node resource quantity, the expected utilization rate of the node resource, the occupied resource quantity of the node on-line service and the reserved resource quantity of the node on-line service.

And the resource occupation adjusting module 430 is configured to adjust the resource occupation amount of the target Pod at the target moment to be the target resource occupation amount in response to determining that the resource increase value does not exceed the available resource amount of the offline task of the target node.

Referring to fig. 5, a resource adjusting apparatus, implemented based on a kubernets system, includes:

the resource increase value determining module 510 is configured to determine a target resource occupation amount to be obtained by the target Pod corresponding to the target offline task at the target time and a historical resource occupation amount at the historical time, and calculate a resource increase value of the target resource occupation amount relative to the historical resource occupation amount.

A resource second determination module 520 configured to determine whether the target Pod is in a resource adjustment cooling phase in response to determining that the resource increase value exceeds the resource increase value threshold.

In some exemplary embodiments, the resource second determining module 520 is specifically configured to:

The resource first determining module 530 is configured to obtain an offline task available resource amount of a target cluster corresponding to a target node corresponding to the target Pod, obtain the offline task available resource amount of the target node in response to determining that the resource added value does not exceed the offline task available resource amount of the target cluster, and determine whether the resource added value exceeds the offline task available resource amount of the target node.

And the resource occupation adjusting module 540 is configured to adjust the resource occupation amount of the target Pod at the target moment to be the target resource occupation amount in response to the determination that the resource increase value does not exceed the available resource amount of the offline task of the target node.

For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, the functionality of the various modules may be implemented in the same one or more software and/or hardware implementations of the present disclosure.

The apparatus in the foregoing embodiment is used to implement the corresponding resource adjustment method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.

Based on the same inventive concept, corresponding to the method of any embodiment described above, the present disclosure further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor executes the program to implement the resource adjustment method described in any embodiment.

Fig. 6 is a schematic diagram illustrating a more specific hardware structure of an electronic device according to this embodiment, where the device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via a bus 1050.

The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present disclosure.

The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static Memory device, a dynamic Memory device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solutions provided by the embodiments of the present specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called by the processor 1010 for execution.

The input/output interface 1030 is used for connecting an input/output module to input and output information. The input/output module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.

The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present device and other devices. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).

Bus 1050 includes a path that transfers information between various components of the device, such as processor 1010, memory 1020, input/output interface 1030, and communication interface 1040.

It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.

The electronic device of the foregoing embodiment is used to implement the corresponding resource adjustment method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.

Based on the same inventive concept, corresponding to any of the above-described embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the resource adjustment method according to any of the above-described embodiments.

The non-transitory computer readable storage medium may be any available medium or data storage device that can be accessed by a computer, including but not limited to magnetic memory (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical memory (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor memory (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND FLASH), Solid State Disks (SSDs)), etc.

The computer instructions stored in the storage medium of the foregoing embodiment are used to enable the computer to execute the resource adjustment method according to any embodiment in the foregoing exemplary method section, and have the beneficial effects of the corresponding method embodiment, which are not described herein again.

As will be appreciated by one skilled in the art, embodiments of the present disclosure may be embodied as a system, method or computer program product. Accordingly, the present disclosure may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.) or a combination of hardware and software, which may be referred to herein generally as a "circuit," module "or" system. Furthermore, in some embodiments, the present disclosure may also be embodied in the form of a computer program product in one or more computer-readable media having computer-readable program code embodied therein.

Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive example) of the computer readable storage medium may include, for example: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer readable medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

Further, while the operations of the disclosed methods are depicted in the drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Rather, the steps depicted in the flowcharts may change order of execution. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.

Use of the verb "comprise", "comprise" and its conjugations does not exclude the presence of elements or steps other than those stated in a claim. The article "a" or "an" preceding an element does not exclude the presence of a plurality of such elements.

While the spirit and principles of the present disclosure have been described with reference to several particular embodiments, it is to be understood that the present disclosure is not limited to the particular embodiments disclosed, nor is the division of aspects, which is for convenience only as the features in such aspects may not be combined to benefit. The disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions.

Claims

1. A resource adjusting method is realized based on a Kubernets system, and is characterized by comprising the following steps:

determining target resource occupation amount of a target Pod corresponding to a target offline task to be acquired at a target moment and historical resource occupation amount at a historical moment, and calculating a resource increase value of the target resource occupation amount relative to the historical resource occupation amount;

acquiring the offline task available resource amount of a target cluster corresponding to a target node corresponding to the target Pod, responding to the fact that the resource added value does not exceed the offline task available resource amount of the target cluster, acquiring the offline task available resource amount of the target node, and judging whether the resource added value exceeds the offline task available resource amount of the target node or not;

2. The method of claim 1, further comprising, after said calculating a resource increase value for said target resource occupancy relative to said historical resource occupancy:

before the obtaining of the available resource amount of the offline task of the target cluster corresponding to the target node corresponding to the target Pod, the method further includes:

3. The method of claim 2, wherein the determining whether the target Pod is in a resource adjustment cooling phase comprises:

4. The method of claim 1, wherein the determining the target resource occupation amount to be obtained by the target Pod corresponding to the target offline task at the target moment comprises:

acquiring the resource quantity demand of the target Pod at the target moment;

5. The method according to claim 1, wherein after determining a target resource occupation amount to be obtained by a target Pod corresponding to the target offline task at a target time, the method further comprises:

6. The method of claim 1, wherein the obtaining of the amount of resources available for the offline task of the target cluster corresponding to the target node corresponding to the target Pod comprises:

acquiring the resource amount of a cluster offline task resource pool of the target cluster;

7. The method of claim 6, wherein the obtaining the cluster offline task pool resource amount of the target cluster comprises:

8. The method according to claim 1, wherein the obtaining of the amount of resources available for the offline task of the target node comprises:

9. The method of claim 6, wherein the obtaining the node offline task resource pool resource amount of the target node comprises:

10. The method of claim 1, wherein the resources comprise at least one of:

CPU, memory, disk IO and network IO.

11. A resource adjusting device, implemented based on a Kubernets system, the device comprising:

12. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the method of any one of claims 1 to 10 when the program is executed by the processor.

13. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 10.