CN114385342A - Container cloud overload protection method and device, computer device and storage medium - Google Patents

Abstract

The present disclosure relates to a container cloud overload protection method and device, a computer device and a storage medium. The container cloud overload protection method includes: obtaining the relevant data of the current logical host and the current business in the container cloud; judging whether the resource usage of the current logical host reaches the overload condition; when the resource usage of the current logical host reaches the overload condition, Create a standby logical host; determine the resource space of the standby logical host according to the relevant data of the current logical host and the current business; allocate resources to the standby logical host according to the resource space of the standby logical host. In the present disclosure, by establishing a dynamic standby resource pool in the container, a dynamic allocation algorithm can be set by using the relevant data of the current Pod and the service, and the resources of the standby Pod can be quantitatively calculated.

Description

Container cloud overload protection method and device, computer device and storage medium

technical field

The present disclosure relates to the field of security, and in particular, to a container cloud overload protection method and device, a computer device and a storage medium.

Background technique

MEC (Multi-access Edge Computing, multi-access edge computing) provides services for enterprises and customers at the edge of the network, sinking computing power and storage capacity to edge nodes, which is conducive to reducing delay and increasing transmission rate, and can improve user experience , Provide third-party application integration, enhance network value, and bring great economic benefits to telecom operators.

Each business in MEC is run based on containers, and the operational computing efficiency is closely related to the size of the resource space. The MEC system uses K8S (Kubernetes cluster) to uniformly manage and orchestrate containers, and allocate resources in units of Pod (the smallest resource management component in Kubernetes, logical host).

SUMMARY OF THE INVENTION

The inventor found through research and development that the allocation of container resources in related technologies is mainly based on qualitative analysis and automatic activation, and it is impossible to accurately and dynamically divide reasonable resources for Pods, which will cause resource waste or low utilization.

In view of at least one of the above technical problems, the present disclosure provides a container cloud overload protection method and device, a computer device, and a storage medium, and a dynamic backup resource pool is established in the container, which can utilize the current Pod and related data devices of services. Determine the dynamic allocation algorithm to quantitatively calculate the resources of the standby Pod.

According to one aspect of the present disclosure, there is provided a container cloud overload protection method, including:

Obtain the relevant data of the current logical host and the current business in the container cloud;

Determine whether the resource usage of the current logical host reaches the overload condition;

When the resource usage of the current logical host reaches the overload condition, create a standby logical host;

Determine the resource space of the standby logical host according to the relevant data of the current logical host and the current business;

Allocate resources to the standby logical host according to the resource space of the standby logical host.

In some embodiments of the present disclosure, the data related to the current logical host and the current service include: the traffic volume in the system, the service weight, the resources actually occupied by the service, the current number of logical hosts and the quintuple of the current logical host resource space parameter.

In some embodiments of the present disclosure, the determining of the resource space of the standby logical host according to the relevant data of the current logical host and the current service includes:

Determine the service resource occupancy according to the service volume in the system, the service weight of each service and the actual occupied resources;

Determine the total resource space of all current logical hosts according to the current number of logical hosts and the resource space of each current logical host;

Determine the resource space of the standby logical host according to the service resource occupancy and the total resource space of all current logical hosts.

In some embodiments of the present disclosure, the determining the service resource occupancy according to the service volume in the system, the service weight of each service and the actual occupied resources includes:

The sum of the product of the service weight of each service and the actual occupied resources is taken as the service resource occupancy.

In some embodiments of the present disclosure, the determining the resource space of the standby logical host according to the occupancy of service resources and the total resource space of all current logical hosts includes:

Divide the maximum value of the business resource occupancy and the total resource space of all current logical hosts by the business volume in the system, as the resource space of the standby logical host.

In some embodiments of the present disclosure, the obtaining the relevant data of the current logical host and the current business in the container cloud includes:

Start the logical host to provide services, and set the business weight of the business according to user input when the business enters the system;

Disperse different services to different logical hosts for execution;

Initialize the number of logical hosts and the current logical host resource space;

Statistics on the resources actually occupied by each service in the system.

In some embodiments of the present disclosure, when the resource usage of the current logical host reaches an overload condition, the container cloud overload protection method further includes:

Store the business request newly entering the system in the cache area, and then perform the steps of creating a standby logical host;

After allocating resources to the standby logical host, it is determined whether to enable the standby logical host and include the buffer request into the standby logical host.

In some embodiments of the present disclosure, the container cloud overload protection method further includes:

When the standby logical host is turned on and the buffer request is included in the standby logical host, each logical host operates normally, and the related data of the current logical host and the current service is updated.

According to another aspect of the present disclosure, a container cloud overload protection device is provided, including:

The data acquisition module is used to acquire the relevant data of the current logical host and the current business in the container cloud;

The overload judgment module is used to judge whether the resource usage of the current logical host reaches the overload condition;

The standby host creation module is used to create a standby logical host when the resource usage of the current logical host reaches the overload condition;

The resource space determination module is used to determine the resource space of the standby logical host according to the relevant data of the current logical host and the current business;

The resource allocation module is used for allocating resources to the standby logical host according to the resource space of the standby logical host.

In some embodiments of the present disclosure, the container cloud overload protection apparatus is configured to perform the operation of implementing the container cloud overload protection method described in any of the foregoing embodiments.

According to another aspect of the present disclosure, there is provided a computer apparatus, comprising:

memory for storing instructions;

The processor is configured to execute the instructions, so that the computer apparatus executes the operation of implementing the container cloud overload protection method according to any of the foregoing embodiments.

According to another aspect of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions that, when executed by a processor, implement the container according to any of the above embodiments Cloud overload protection methods.

In the present disclosure, by establishing a dynamic standby resource pool in the container, a dynamic allocation algorithm can be set by using the relevant data of the current Pod and the service, and the resources of the standby Pod can be quantitatively calculated.

Description of drawings

In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only These are some embodiments of the present disclosure, and for those of ordinary skill in the art, other drawings can also be obtained from these drawings without creative effort.

FIG. 1 is a schematic diagram of some embodiments of the disclosed container cloud overload protection method.

FIG. 2 is a schematic diagram of other embodiments of the disclosed container cloud overload protection method.

FIG. 3 is a schematic diagram of some embodiments of the disclosed container cloud overload protection device.

FIG. 4 is a schematic diagram of some embodiments of the disclosed computer apparatus.

Detailed ways

The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, but not all of the embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application or uses in any way. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present disclosure.

The relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

Meanwhile, it should be understood that, for the convenience of description, the dimensions of various parts shown in the accompanying drawings are not drawn in an actual proportional relationship.

Techniques, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the authorized description.

In all examples shown and discussed herein, any specific value should be construed as illustrative only and not as limiting. Accordingly, other examples of exemplary embodiments may have different values.

It should be noted that like numerals and letters refer to like items in the following figures, so once an item is defined in one figure, it does not require further discussion in subsequent figures.

FIG. 1 is a schematic diagram of some embodiments of the disclosed container cloud overload protection method. Preferably, this embodiment can be executed by the container cloud overload protection device of the present disclosure, the computer device of the present disclosure, or the container management manager node of the present disclosure. The method may include steps 11-15, wherein:

Step 11: Acquire data related to the current logical host Pod and the current business in the container cloud.

In some embodiments of the present disclosure, the data related to the current logical host and the current service may include: the traffic volume ω in the system, the service weight γ _e , the resource actually occupied by the service β _e , the current logical host number τ and the current logical The quintuple parameters {ω, τ, μ _i , β _e , γ _e } of the host resource space μ _i .

In some embodiments of the present disclosure, step 11 may include steps 111 to 114, wherein:

Step 111 , K8S starts the logical host Pod to provide services, and when the business enters the system, the business weight γ _e of the business is set according to the user input.

In some embodiments of the present disclosure, step 111 may include: when the service enters the system, the user defines the service weight γ _e , where γ _e ∈(0,1), the value may be defined by the user according to the priority and The importance is customized, the higher the weight, the higher the priority.

Step 112: Disperse different services to different logical hosts for execution.

Step 113: Initialize the number of logical hosts τ and the current logical host resource space μ _i .

In some embodiments of the present disclosure, step 113 may include: initializing the Pod number τ and the Pod resource μ _i according to a Yaml file (a type of configuration file) defining Pod resources.

Step 114: Count the resources β _e actually occupied by each service in the system.

In some embodiments of the present disclosure, the resource β _e may include CPU usage, memory usage, disk I/O usage, network bandwidth usage, and the like.

In some embodiments of the present disclosure, step 114 may include: the node node in the container includes a monitoring program, and the monitoring program is used to continuously monitor the resource usage of the Pod and report it to the Manager node for analysis, and the resource usage of the Pod involves different services , so the resource β _e occupied by each service also uses the Manager node for analysis and statistics. The resource usage here includes CPU usage, memory usage, disk I/O usage, and network bandwidth usage.

Step 12, judging whether the resource usage of the current logical host reaches the overload condition.

Step 13: Create a standby logical host when the resource usage of the current logical host reaches the overload condition.

Step 14: Determine the resource space of the standby logical host according to the relevant data of the current logical host and the current service.

In some embodiments of the present disclosure, step 14 may include steps 141-143, wherein:

Step 141: Determine the service resource occupancy A according to the service volume in the system, the service weight of each service, and the actual occupied resources.

In some embodiments of the present disclosure, step 141 may include: summing the product of the service weight of each service and the actual occupied resources, as the service resource occupancy A.

In some embodiments of the present disclosure, step 141 may include: determining the service resource occupancy A according to formula (1).

Step 142: Determine the total resource space B of all current logical hosts according to the current number of logical hosts and the resource space of each current logical host.

In some embodiments of the present disclosure, step 142 may include: determining the total resource space B of all current logical hosts according to formula (2).

Step 143: Determine the resource space k _pod of the standby logical host according to the service resource occupancy A and the total resource space B of all current logical hosts.

In some embodiments of the present disclosure, step 143 may include: dividing the maximum value among the service resource occupancy A and the total resource space B of all current logical hosts by the service volume in the system, as the resource space k of the standby logical host _pods .

In some embodiments of the present disclosure, step 143 may include: determining the resource space k _pod of the standby logical host according to formula (3).

In the above embodiments of the present disclosure, considering that the space of the spare Pod should be able to process part or all of the cache request, the maximum value of the two calculation results should be selected as the reference value of the spare Pod resource pool.

Step 15: Allocate resources to the standby logical host according to the resource space of the standby logical host.

In some embodiments of the present disclosure, the created standby Pod may be included in the cache request.

Based on the container cloud overload protection method provided by the above embodiments of the present disclosure, an improved idea for dynamic resource allocation of container clouds due to overload is provided. and business-related data to set a dynamic allocation algorithm to quantitatively calculate the resources of spare Pods, thus solving the problems of container paralysis and invalid requests when resources are insufficient, as well as the waste or shortage of resources in the case of automatic allocation, which contributes to the stability of the container cloud. security and safety.

The container cloud overload protection method for dynamically allocating resources provided by the above embodiments of the present disclosure. Aiming at the shortage of container cloud resources and the defects of fixed resource allocation, a dynamic allocation algorithm for quantitative computing resources is proposed. The above-mentioned embodiments of the present disclosure utilize the quintuple {ω,τ,μi, _βe , _γe } composed of five elements, such as the number of services, the priority, the amount of occupied resources, the number of Pods, and the Pod resources in the container cloud _. , to construct a dynamic and quantitative standby Pod resource pool, which can realize flexible allocation and rational use of resources.

FIG. 2 is a schematic diagram of other embodiments of the disclosed container cloud overload protection method. Preferably, this embodiment can be executed by the container cloud overload protection device of the present disclosure, the computer device of the present disclosure, or the container management manager node of the present disclosure. Steps 21 and 22 in the embodiment of FIG. 2 are respectively the same as or similar to steps 11 and 12 in the embodiment of FIG. 1 . The method of the embodiment of FIG. 2 may include steps 21 to 24, wherein:

Step 21: Acquire data related to the current logical host Pod and the current business in the container cloud.

In some embodiments of the present disclosure, the data related to the current logical host and the current service include: the traffic volume ω in the system, the service weight γ _e , the resource actually occupied by the service β _e , the current logical host number τ and the current logical host The quintuple parameters {ω,τ,μ _i ,β _e ,γ _e } of the resource space μ _i .

Step 22, judging whether the resource usage of the current logical host reaches the overload condition.

Step 23: In the case that the resource usage of the current logical host reaches the overload condition, overload protection is performed on the container.

In some embodiments of the present disclosure, step 23 may include: if the analysis result of the Manager shows that the resource usage of the Pod has reached an overload condition, the Manager issues an instruction to protect the container from overload.

In some embodiments of the present disclosure, when the resource usage of the current logical host reaches an overload condition, the container cloud overload protection method further includes:

In some embodiments of the present disclosure, in step 23, the step of performing overload protection on the container may include steps 231 to 233, wherein:

Step 231: Store the service request newly entering the system in the cache area.

Step 232: Create a standby logical host; determine the resource space of the standby logical host according to the current logical host and relevant data of the current service; allocate resources to the standby logical host according to the resource space of the standby logical host.

In some embodiments of the present disclosure, step 232 may include steps 13-15 in the embodiment of FIG. 1 .

In some embodiments of the present disclosure, step 232 may include: the overload protection enters the judgment module, the Manager node uses the dynamic allocation algorithm, and adopts the formula (3) according to the quintuple parameters {ω,τ,μ _i ,β _e ,γ _e } Quantitatively and dynamically allocate resources to spare Pods.

Step 233, after allocating resources to the standby logical host, determine whether to enable the standby logical host and include the buffer request into the standby logical host.

In some embodiments of the present disclosure, step 233 may include: after the Manager node allocates resources to the standby Pod, determining whether to enable the standby Pod at this time and include the buffer request in the Pod.

Step 24 , when the overload protection mechanism is completed (opening the standby logical host and incorporating the buffer request into the standby logical host), each logical host operates normally, and the current logical host and relevant data of the current service are updated.

In some embodiments of the present disclosure, step 24 may include: completing the overload protection mechanism after dynamic resource allocation of the standby Pod, and each Pod running services normally. At this time, the quintuple {ω, τ, μ _i , β _e , γ _e } needs to be updated to provide parameter support for the next expansion of Pod resources.

In some embodiments of the present disclosure, step 24 may include: according to the above process, updating the quintuple parameters {ω, τ, μ _i , β _e , γ _e }, where ω, β _e , γ _e should be entered according to the updated The business and business resources of the system are determined and updated, τ=τ+1, and μ _i should be included in the resource k _pod of this standby Pod. Since each parameter is related to services and Pods in the system, the foregoing embodiments of the present disclosure can implement dynamic and calculable spare Pod resource capacity.

The above embodiments of the present disclosure improve the dynamic resource allocation method of the container cloud, and use the dynamic allocation algorithm to quantitatively calculate the resources of the standby Pod, which is reasonable and scientific. Aiming at the defects of qualitative analysis or fixed resource allocation in the container cloud dynamic resource allocation scheme, this scheme uses the number of services, service weights, service resources, the number of Pods and Pod resources to form a quintuple {ω,τ,μ _i ,β _e ,γ _e }, and based on this quintuple, a dynamic allocation algorithm is proposed to calculate resource capacity for the standby Pod, which solves the problem of insufficient resources or waste of resources caused by the fixed allocation of resources in the current scheme, and solves the problem of the lack of scientificity in the qualitative analysis of overload protection in other schemes. The problem.

The determination module in the overload protection mechanism of the above embodiments of the present disclosure proposes a dynamic allocation algorithm, which involves five elements in total, namely: the traffic volume ω in the system, the traffic weight γ _e , the actual occupied resources β _e of the service, and the current Given the number of Pods τ and Pod resources μ _i , it is reasonable to use the existing data to calculate the resources that should be allocated to the spare Pod in the above embodiments of the present disclosure.

The above-mentioned embodiments of the present disclosure allocate resources to the spare Pod based on the quantitative calculation result, which is beneficial to the rational use of system resources and improves the resource utilization rate in the Pod.

The decision-making module of the above-mentioned embodiment of the present disclosure is the enabling "switch" of the standby Pod, which controls whether the Pod that has allocated resources is turned on, and further processes the cache request as the Pod is enabled.

FIG. 3 is a schematic diagram of some embodiments of the container cloud overload protection device of the present disclosure. As shown in FIG. 3 , the container cloud overload protection device of the present disclosure may include a data acquisition module 31, an overload judgment module 32, a backup host creation module 33, a resource space determination module 34, and a resource allocation module 35, wherein:

The data acquisition module 31 is configured to acquire relevant data of the current logical host and the current business in the container cloud.

In some embodiments of the present disclosure, the data related to the current logical host and the current service may include: the traffic volume ω in the system, the service weight γ _e , the resource actually occupied by the service β _e , the current logical host number τ and the current logical The quintuple parameters {ω, τ, μ _i , β _e , γ _e } of the host resource space μ _i .

In some embodiments of the present disclosure, the service weight γ _e ∈ (0, 1), the value can be customized by the user according to the priority and importance of the service, and the higher the weight, the higher the priority.

In some embodiments of the present disclosure, the data acquisition module 31 can be used to start a logical host to provide services, and when a business enters the system, set the business weight of the business according to user input; disperse different services to different logical hosts The number of logical hosts and the current logical host resource space are initialized; the resources actually occupied by each service in the system are counted.

In some embodiments of the present disclosure, the data acquisition module 31 may be configured to initialize the Pod number τ and the Pod resource μ _i according to a Yaml file (a type of configuration file) that defines Pod resources.

In some embodiments of the present disclosure, the node node in the container includes a monitoring program, and the monitoring program is used to continuously monitor the resource usage of the Pod and report it to the Manager node for analysis. The resource usage of the Pod involves different services, so each service occupies The resource β _e also uses the Manager node for analysis and statistics. The resource usage here includes CPU usage, memory usage, disk I/O usage, and network bandwidth usage.

The overload judgment module 32 is used for judging whether the resource usage of the current logical host reaches the overload condition.

The standby host creation module 33 is configured to create a standby logical host when the resource usage of the current logical host reaches an overload condition.

The resource space determination module 34 is configured to determine the resource space of the standby logical host according to the relevant data of the current logical host and the current service.

In some embodiments of the present disclosure, the resource space determination module 34 may be configured to determine the service resource occupancy according to the service volume in the system, the service weight of each service and the actual occupied resources; according to the current number of logical hosts and each current logical The resource space of the host determines the total resource space of all current logical hosts; the resource space of the standby logical host is determined according to the occupancy of business resources and the total resource space of all current logical hosts.

In some embodiments of the present disclosure, the resource space determination module 34 may be configured to sum the product of the service weight of each service and the actual occupied resources, as the service resource occupancy A.

In some embodiments of the present disclosure, the resource space determination module 34 may be configured to determine the service resource occupancy A according to formula (1).

In some embodiments of the present disclosure, the resource space determination module 34 may be configured to determine the total resource space B of all current logical hosts according to formula (2).

In some embodiments of the present disclosure, the resource space determination module 34 may be configured to divide the maximum value of the service resource occupancy and the total resource space of all current logical hosts by the service volume in the system, as the resource space of the standby logical host .

In some embodiments of the present disclosure, the resource space determination module 34 may be configured to determine the resource space k _pod of the standby logical host according to formula (3).

The resource allocation module 35 is configured to allocate resources to the standby logical host according to the resource space of the standby logical host.

In some embodiments of the present disclosure, the container cloud overload protection device can also be used to store the newly entered service request into the system in the cache area when the resource usage of the current logical host reaches the overload condition, and then execute the creation The operation of the standby logical host; after allocating resources to the standby logical host, it is judged whether to start the standby logical host and include the buffer request in the standby logical host.

In some embodiments of the present disclosure, the container cloud overload protection device may also be used to enable each logical host to run normally, update the current logical host and Data about the current business.

In some embodiments of the present disclosure, the container cloud overload protection device can also be used to complete the overload protection mechanism after dynamic resource allocation of spare Pods, and each Pod runs services normally. At this time, the quintuple {ω, τ, μ _i , β _e , γ _e } needs to be updated to provide parameter support for the next expansion of Pod resources.

In some embodiments of the present disclosure, the container cloud overload protection device may also be used to update the quintuple parameters {ω, τ, μ _i , β _e , γ _e } according to the above process, where ω, β _e , γ e , γ _e should be determined and updated according to the business and business resources entering the system, τ=τ+1, and μ _i should be included in the resource k _pod of this standby Pod. Since each parameter is related to services and Pods in the system, the foregoing embodiments of the present disclosure can implement dynamic and calculable spare Pod resource capacity.

In some embodiments of the present disclosure, the container cloud overload protection device may be configured to perform operations for implementing the container cloud overload protection method described in any of the foregoing embodiments (eg, the embodiment in FIG. 1 or FIG. 2 ).

In some embodiments of the present disclosure, the container cloud overload protection device may be implemented as a container management Manager node of the present disclosure.

Based on the container cloud overload protection device provided by the above embodiments of the present disclosure, an improved idea for dynamic resource allocation of container clouds due to overload is provided. and business-related data to set a dynamic allocation algorithm to quantitatively calculate the resources of spare Pods, thus solving the problems of container paralysis and invalid requests when resources are insufficient, as well as the waste or shortage of resources in the case of automatic allocation, which contributes to the stability of the container cloud. security and safety.

The determination module in the overload protection mechanism of the above embodiments of the present disclosure proposes a dynamic allocation algorithm, which involves five elements in total, namely: the traffic volume ω in the system, the traffic weight γ _e , the actual occupied resources β _e of the service, and the current Given the number of Pods τ and Pod resources μ _i , it is reasonable to use the existing data to calculate the resources that should be allocated to the spare Pod in the above embodiments of the present disclosure.

The above-mentioned embodiments of the present disclosure allocate resources to the spare Pod based on the quantitative calculation result, which is beneficial to the rational use of system resources and improves the resource utilization rate in the Pod.

The decision-making module of the above-mentioned embodiment of the present disclosure is the enabling "switch" of the standby Pod, which controls whether the Pod that has allocated resources is turned on, and further processes the cache request as the Pod is enabled.

FIG. 4 is a schematic diagram of some embodiments of the disclosed computer apparatus. As shown in FIG. 4, the computer apparatus of the present disclosure may include a memory 41 and a processor 42, wherein:

The memory 41 is used to store instructions.

The processor 42 is configured to execute the instructions, so that the computer apparatus executes the operation of implementing the container cloud overload protection method described in any of the foregoing embodiments (for example, the embodiment of FIG. 1 or FIG. 2 ).

In some embodiments of the present disclosure, the computer apparatus may be implemented as a container management Manager node of the present disclosure.

The container cloud overload protection solution for dynamically allocating resources provided by the above embodiments of the present disclosure. Aiming at the shortage of container cloud resources and the shortcomings of fixed resource allocation, a dynamic allocation method for quantitative computing resources is proposed. The above-mentioned embodiments of the present disclosure utilize the quintuple {ω,τ,μi, _βe , _γe } composed of five elements, such as the number of services, the priority, the amount of occupied resources, the number of Pods, and the Pod resources in the container cloud _. , to construct a dynamic and quantitative standby Pod resource pool, which can realize flexible allocation and rational use of resources.

According to another aspect of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions that, when executed by a processor, implement any of the foregoing embodiments (eg, FIG. 1 ). or the container cloud overload protection method described in the embodiment of FIG. 2 .

Based on the computer-readable storage medium provided by the above embodiments of the present disclosure, it is reasonable and scientific to use the dynamic allocation algorithm to quantitatively calculate the resources of the spare Pod. Aiming at the defects of qualitative analysis or fixed resource allocation in the container cloud dynamic resource allocation scheme, this scheme uses the number of services, service weights, service resources, the number of Pods and Pod resources to form a quintuple {ω,τ,μ _i ,β _e ,γ _e }, and based on this quintuple, a dynamic allocation algorithm is proposed to calculate resource capacity for the standby Pod, which solves the problem of insufficient resources or waste of resources caused by the fixed allocation of resources in the current scheme, and solves the problem of the lack of scientificity in the qualitative analysis of overload protection in other schemes. The problem.

The container cloud overload protection device described above can be implemented as a general purpose processor, a programmable logic controller (PLC), a digital signal processor (DSP), an application specific integrated circuit (ASIC), Field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or any suitable combination thereof.

So far, the present disclosure has been described in detail. Some details that are well known in the art are not described in order to avoid obscuring the concept of the present disclosure. Those skilled in the art can fully understand how to implement the technical solutions disclosed herein based on the above description.

Those of ordinary skill in the art can understand that all or part of the steps of implementing the above embodiments can be completed by hardware, and can also be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. The storage medium mentioned may be a read-only memory, a magnetic disk or an optical disk, etc.

The description of the present disclosure has been presented for purposes of example and description, and is not intended to be exhaustive or to limit the disclosure to the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiment was chosen and described in order to better explain the principles of the disclosure and the practical application, and to enable others of ordinary skill in the art to understand the disclosure for various embodiments with various modifications as are suited to the particular use.

Claims (12)

1. A container cloud overload protection method, comprising: Obtain the relevant data of the current logical host and the current business in the container cloud; Determine whether the resource usage of the current logical host reaches the overload condition; When the resource usage of the current logical host reaches the overload condition, create a standby logical host; Determine the resource space of the standby logical host according to the relevant data of the current logical host and the current business; Allocate resources to the standby logical host according to the resource space of the standby logical host. 2. The container cloud overload protection method according to claim 1, wherein the relevant data of the current logical host and the current business comprises: the traffic volume in the system, the business weight, the actual resources occupied by the business, the number of the current logical host and a quintuple parameter of the current logical host resource space. 3. The container cloud overload protection method according to claim 2, wherein the determining the resource space of the standby logical host according to the relevant data of the current logical host and the current business comprises: Determine the service resource occupancy according to the service volume in the system, the service weight of each service and the actual occupied resources; Determine the total resource space of all current logical hosts according to the current number of logical hosts and the resource space of each current logical host; Determine the resource space of the standby logical host according to the service resource occupancy and the total resource space of all current logical hosts. 4. The container cloud overload protection method according to claim 3, wherein the determining the service resource occupancy according to the service volume in the system, the service weight of each service and the actual occupied resources comprises: The sum of the product of the service weight of each service and the actual occupied resources is taken as the service resource occupancy. 5. The container cloud overload protection method according to claim 3, wherein the determining the resource space of the standby logical host according to the service resource occupancy and the total resource space of all current logical hosts comprises: Divide the maximum value of the business resource occupancy and the total resource space of all current logical hosts by the business volume in the system, as the resource space of the standby logical host. 6. The container cloud overload protection method according to any one of claims 1-5, wherein the acquiring the relevant data of the current logical host and the current business in the container cloud comprises: Start the logical host to provide services, and set the business weight of the business according to user input when the business enters the system; Disperse different services to different logical hosts for execution; Initialize the number of logical hosts and the current logical host resource space; Statistics on the resources actually occupied by each service in the system. 7. The container cloud overload protection method according to any one of claims 1-5, characterized in that, when the resource usage of the current logical host reaches the overload condition, the method further comprises: Store the business request newly entering the system in the cache area, and then perform the steps of creating a standby logical host; After allocating resources to the standby logical host, it is determined whether to enable the standby logical host and include the buffer request into the standby logical host. 8. The container cloud overload protection method according to claim 7, further comprising: When the standby logical host is turned on and the buffer request is included in the standby logical host, each logical host operates normally, and the related data of the current logical host and the current service is updated. 9. A container cloud overload protection device, comprising: The data acquisition module is used to acquire the relevant data of the current logical host and the current business in the container cloud; The overload judgment module is used to judge whether the resource usage of the current logical host reaches the overload condition; The standby host creation module is used to create a standby logical host when the resource usage of the current logical host reaches the overload condition; The resource space determination module is used to determine the resource space of the standby logical host according to the relevant data of the current logical host and the current business; The resource allocation module is used for allocating resources to the standby logical host according to the resource space of the standby logical host. 10 . The container cloud overload protection device according to claim 9 , wherein the container cloud overload protection device is configured to perform an operation of implementing the container cloud overload protection method according to any one of claims 1 to 8 . . 11. A computer device, comprising: memory for storing instructions; A processor, configured to execute the instructions, so that the computer apparatus executes an operation for implementing the container cloud overload protection method according to any one of claims 1-8. 12. A computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and when the instructions are executed by a processor, the container cloud according to any one of claims 1-8 is implemented overload protection method.

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