CN115145691A - Container scheduling system and method based on kubernets multi-cluster - Google Patents

Container scheduling system and method based on kubernets multi-cluster Download PDF

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CN115145691A
CN115145691A CN202210797835.9A CN202210797835A CN115145691A CN 115145691 A CN115145691 A CN 115145691A CN 202210797835 A CN202210797835 A CN 202210797835A CN 115145691 A CN115145691 A CN 115145691A
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container
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种保中
李明
李胜
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Suzhou Sicui Industrial Internet Technology Research Institute Co ltd
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    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
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    • G—PHYSICS
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    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
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Abstract

The invention discloses a container scheduling system and method based on kubernets multi-cluster, belonging to the technical field of cloud computing, aiming at solving the technical problem of realizing the automatic deployment and scheduling of containers of a kubernets multi-cluster operation and management platform in multi-cluster, and adopting the technical scheme that: the system comprises a cluster receiving and managing unit, a cluster information detection unit, a cluster container scheduling unit and a storage unit. The method comprises the following specific steps: acquiring one or more schedulable clusters according to the cluster information detection unit to obtain a schedulable cluster list; screening the schedulable cluster list through a cluster preselection algorithm to obtain a cluster list capable of deploying a container as an available cluster list; performing integral calculation on clusters in the available cluster list through a cluster optimization algorithm, and sequencing the clusters according to the integral obtained by the clusters to obtain a cluster deployment list; and sequentially selecting the clusters from the cluster deployment list to deploy the containers according to the cluster deployment list and the needs of the clusters in the cluster deployment strategy.

Description

基于kubernets多集群的容器调度系统及方法Container scheduling system and method based on kubernets multi-cluster

技术领域technical field

本发明涉及云计算技术领域，具体地说是一种基于kubernets多集群的容器调度系统及方法。The invention relates to the technical field of cloud computing, in particular to a container scheduling system and method based on kubernets multi-cluster.

背景技术Background technique

Kubernetes是目前云计算基础技术重要的一环，使用场景非常广泛。开发人员可以通过使用Kubernetes来作为云计算平台底层的实现，随着云计算和互联网时代的发展，产生了越来越多的云平台，同时促进了kubernets多集群运管平台的产生。但是当前的kubernets多集群运管平台一般只能提供单个集群中容器的部署和多个集群下容器的统一部署，不能实现多个kubernets集群下容器的自动化调度部署，而单个集群容器部署存在多个冗余操作，多个集群统一部署需要工作人员人为判断容器与集群的适配度，增加了工作人员的技术难度和工作成本。Kubernetes is an important part of the current cloud computing basic technology, and the use scenarios are very wide. Developers can use Kubernetes as the bottom layer of the cloud computing platform. With the development of cloud computing and the Internet era, more and more cloud platforms have emerged, and at the same time, the emergence of Kubernetes multi-cluster operation and management platforms has been promoted. However, the current kubernets multi-cluster operation and management platform generally can only provide the deployment of containers in a single cluster and the unified deployment of containers under multiple clusters, and cannot realize the automatic scheduling and deployment of containers under multiple kubernets clusters, and there are multiple container deployments in a single cluster. Redundant operations and unified deployment of multiple clusters require staff to manually judge the suitability of containers and clusters, which increases the technical difficulty and work cost of staff.

故如何实现kubernets多集群运管平台容器在多集群中自动化部署调度，降低了工作人员的技术难度和工作成本是目前亟待解决的技术问题。Therefore, how to realize the automatic deployment and scheduling of kubernets multi-cluster operation and management platform containers in multi-cluster and reduce the technical difficulty and work cost of staff is a technical problem that needs to be solved urgently.

发明内容SUMMARY OF THE INVENTION

本发明的技术任务是提供一种基于kubernets多集群的容器调度系统及方法，来解决目前kubernets多集群运管平台不能实现多个kubernets集群下容器的自动化调度部署的问题。The technical task of the present invention is to provide a container scheduling system and method based on kubernets multi-cluster, to solve the problem that the current kubernets multi-cluster operation and management platform cannot realize the automatic scheduling and deployment of containers under multiple kubernets clusters.

本发明的技术任务是按以下方式实现的，一种基于kubernets多集群的容器调度系统，该系统包括集群纳管单元、集群信息检测单元、集群容器调度单元和存储单元；The technical task of the present invention is achieved in the following manner, a container scheduling system based on kubernets multi-cluster, the system includes a cluster management unit, a cluster information detection unit, a cluster container scheduling unit and a storage unit;

其中，集群纳管单元用于对不同环境下的kubernets集群进行统一的纳管，并把集群的状态信息存放到存储单元；Among them, the cluster management unit is used to uniformly manage the kubernets clusters in different environments, and store the status information of the cluster in the storage unit;

集群信息检测单元用于实时检测和收集被集群纳管单元纳管的kubernets集群的状态信息；The cluster information detection unit is used to detect and collect the status information of the kubernets cluster managed by the cluster management unit in real time;

集群容器调度单元用于解析集群容器部署策略的部署信息，分别使用集群预选算法和集群优先算法对集群进行筛选和积分计算；并根据集群容器部署策略对集群的需要进行容器在多集群中的自动化调度部署；The cluster container scheduling unit is used to analyze the deployment information of the cluster container deployment strategy, and use the cluster pre-selection algorithm and the cluster priority algorithm to filter and integrate the cluster respectively; and according to the cluster container deployment strategy, the cluster is required to automate containers in multiple clusters scheduling deployment;

存储单元用于存储集群容器部署策略、集群状态等需要存储的相关信息。The storage unit is used to store related information that needs to be stored, such as cluster container deployment policies and cluster status.

作为优选，集群容器调度单元的工作过程具体如下：Preferably, the working process of the cluster container scheduling unit is as follows:

(1)、解析集群容器的部署策略；(1), analyze the deployment strategy of cluster containers;

(2)、判断是否存在多种集群预选信息：(2), determine whether there is a variety of cluster pre-selection information:

①、若是，则集群优选算法根据预选信息筛选集群，并跳转至步骤(4)；1. If yes, the cluster optimization algorithm filters the clusters according to the pre-selection information, and jumps to step (4);

②、若否，则执行步骤(3)；②, if not, execute step (3);

(3)、判断是否设置优先级字段：(3), determine whether to set the priority field:

①、若是，则使用设置的优先级筛选集群，并跳转至步骤(4)；①. If yes, use the set priority to filter the cluster and jump to step (4);

②、若否，则使用默认优先级和信息筛选集群，并跳转至步骤(4)；②. If not, use the default priority and information to filter the cluster, and jump to step (4);

(4)、获取可用集群列表；(4), get the list of available clusters;

(5)、对可用集群列表中的集群进行积分计算，公式如下：(5) Integrate the clusters in the list of available clusters, and the formula is as follows:

集群最终得分＝优选算法一得分*优选算法一权重+优选算法二得分*优选算法二权重；The final score of the cluster = the score of the optimal algorithm 1 * the weight of the optimal algorithm 1 + the score of the optimal algorithm 2 * the weight of the optimal algorithm 2;

其中，优选算法一是根据集群容器部署策略中容器在集群中的部署副本数和可用列表集群中集群的节点数量计算得分；具体如下：Among them, the first optimal algorithm is to calculate the score according to the number of deployed copies of the container in the cluster and the number of nodes in the cluster in the available list cluster in the cluster container deployment strategy; the details are as follows:

当集群的节点数量大于集群容器的副本数量时，集群节点数量越多，集群的得分越低；When the number of nodes in the cluster is greater than the number of replicas in the cluster container, the higher the number of cluster nodes, the lower the score of the cluster;

当集群的节点数量小于集群容器的副本数量时，集群的节点数量越少，集群得分越低；When the number of nodes in the cluster is less than the number of replicas in the cluster container, the fewer the number of nodes in the cluster, the lower the cluster score;

集群的节点数量小于集群容器副本数量的集群得分均小于集群节点数量大于容器副本数量的集群得分；The scores of clusters whose number of nodes is less than the number of cluster container replicas are all lower than those of clusters whose number of cluster nodes is greater than the number of container replicas;

通过优选算法一选择出集群节点数量与集群容器的副本数量最相近的节点，提高了集群在部署容器时对节点的筛选效率；Through the first optimization algorithm, the node whose number of cluster nodes is most similar to the number of replicas of cluster containers is selected, which improves the screening efficiency of nodes when the cluster deploys containers;

优选算法二是根据集群剩余的CPU和内存的平均值对集群进行积分计算；The second preferred algorithm is to perform an integral calculation on the cluster according to the average value of the remaining CPU and memory of the cluster;

其中，集群所剩余的CPU和内存的平均值越大，集群的所得积分越高；反之，集群所剩余的CPU和内存的平均值越小，集群的所得积分越低；Among them, the larger the average value of the remaining CPU and memory of the cluster, the higher the score of the cluster; on the contrary, the smaller the average value of the remaining CPU and memory of the cluster, the lower the score of the cluster;

(6)、通过集群积分计算公式、优选算法一得分和优选算法二得分计算出集群的最终得分；(6), calculate the final score of the cluster through the cluster integral calculation formula, the first score of the preferred algorithm and the second score of the preferred algorithm;

(7)、根据每个集群的最终得分对可用集群列表中的集群进行排序，得到集群部署列表。(7) Sort the clusters in the available cluster list according to the final score of each cluster to obtain a cluster deployment list.

一种基于kubernets多集群的容器调度方法，该方法具体如下：A container scheduling method based on kubernets multi-cluster, the method is as follows:

根据集群信息检测单元获取一个或多个可调度的集群，得到可调度的集群列表；Obtain one or more schedulable clusters according to the cluster information detection unit, and obtain a list of schedulable clusters;

通过集群预选算法对可调度的集群列表进行筛选，得到能够部署容器的集群列表为可用集群列表；The list of schedulable clusters is filtered through the cluster preselection algorithm, and the list of clusters that can deploy containers is obtained as the list of available clusters;

通过集群优选算法对可用集群列表中的集群进行积分计算，根据集群所得积分，对集群进行排序，得到集群部署列表；The cluster optimization algorithm is used to calculate the points of the clusters in the list of available clusters, and according to the points obtained by the clusters, the clusters are sorted to obtain the cluster deployment list;

根据集群部署列表和集群部署策略中对集群的需要，依次从集群部署列表中选择集群进行容器的部署。According to the cluster deployment list and the cluster deployment strategy, select clusters from the cluster deployment list to deploy containers.

作为优选，可调度的集群列表的获取过程具体如下：Preferably, the process of obtaining the schedulable cluster list is as follows:

实时的监测已经被集群纳管单元纳管的集群的状态信息；具体如下：Real-time monitoring of the status information of the clusters that have been managed by the cluster management unit; the details are as follows:

若集群的状态信息为健康状态，则为可调度集群；If the state information of the cluster is healthy, it is a schedulable cluster;

若集群的状态信息为非健康状态，则为不可调度集群。If the state information of the cluster is unhealthy, it is an unschedulable cluster.

作为优选，通过集群预选算法对可调度的集群列表进行筛选，得到能够部署容器的集群列表为可用集群列表具体如下：Preferably, the list of schedulable clusters is filtered through the cluster preselection algorithm, and the list of clusters that can deploy containers is obtained as the list of available clusters. The details are as follows:

通过集群容器调度单元解析集群容器的部署策略，根据集群容器部署策略选择合适的集群预选算法对可调度集群列表进行筛选工作得到可用集群列表；The deployment strategy of the cluster container is analyzed by the cluster container scheduling unit, and an appropriate cluster preselection algorithm is selected according to the cluster container deployment strategy to filter the list of schedulable clusters to obtain a list of available clusters;

集群预选算法根据集群容器部署策略中所设置的集群名称、集群标签和集群分组信息对可调度集群列表进行筛选；The cluster preselection algorithm filters the list of schedulable clusters according to the cluster name, cluster label and cluster grouping information set in the cluster container deployment policy;

集群容器部署策略中所设置的集群名称、集群标签和集群分组信息均设置多条，例如：可以设置两条集群名称；同时集群容器部署策略中所设置的集群名称、集群标签和集群分组信息单独使用或组合使用；在组合使用时，筛选符合所有条件的集群，并通过算法优先级字段设置集群名称、集群标签和集群分组信息在集群预选算法中执行的优先级，通过合理的设置算法优先级有效的提高集群预选算法的执行效率；The cluster name, cluster label and cluster group information set in the cluster container deployment strategy are set in multiple pieces, for example, two cluster names can be set; at the same time, the cluster name, cluster label and cluster group information set in the cluster container deployment strategy are separate Use or use in combination; when used in combination, filter clusters that meet all conditions, and set the priority of cluster name, cluster label and cluster grouping information in the cluster pre-selection algorithm through the algorithm priority field, and set the priority of the algorithm reasonably. Effectively improve the execution efficiency of the cluster preselection algorithm;

所有通过集群预选算法的集群形成可用集群列表。All clusters that pass the cluster preselection algorithm form the list of available clusters.

更优地，通过集群优选算法对可用集群列表中的集群进行积分计算，根据集群所得积分，对集群进行排序，得到集群部署列表具体如下：More preferably, the cluster optimization algorithm is used to calculate the points of the clusters in the list of available clusters, and according to the points obtained by the clusters, the clusters are sorted, and the cluster deployment list is obtained as follows:

对可用集群列表中的集群进行积分计算，公式如下：Integrate the clusters in the list of available clusters, and the formula is as follows:

集群最终得分＝优选算法一得分*优选算法一权重+优选算法二得分*优选算法二权重；The final score of the cluster = the score of the optimal algorithm 1 * the weight of the optimal algorithm 1 + the score of the optimal algorithm 2 * the weight of the optimal algorithm 2;

通过集群积分计算公式、优选算法一得分和优选算法二得分计算出集群的最终得分，根据每个集群的最终得分对可用集群列表中的集群进行排序，得到集群部署列表。The final score of the cluster is calculated by the cluster integral calculation formula, the score of the first optimization algorithm and the score of the second optimization algorithm, and the clusters in the available cluster list are sorted according to the final score of each cluster to obtain the cluster deployment list.

更优地，优选算法一是根据集群容器部署策略中容器在集群中的部署副本数和可用列表集群中集群的节点数量计算得分；具体如下：More preferably, the first preferred algorithm is to calculate the score according to the number of deployed replicas of the container in the cluster and the number of nodes in the cluster in the available list cluster in the cluster container deployment strategy; the details are as follows:

当集群的节点数量大于集群容器的副本数量时，集群节点数量越多，集群的得分越低；When the number of nodes in the cluster is greater than the number of replicas in the cluster container, the higher the number of cluster nodes, the lower the score of the cluster;

当集群的节点数量小于集群容器的副本数量时，集群的节点数量越少，集群得分越低；When the number of nodes in the cluster is less than the number of replicas in the cluster container, the fewer the number of nodes in the cluster, the lower the cluster score;

集群的节点数量小于集群容器副本数量的集群得分均小于集群节点数量大于容器副本数量的集群得分；The scores of clusters whose number of nodes is less than the number of cluster container replicas are all lower than those of clusters whose number of cluster nodes is greater than the number of container replicas;

通过优选算法一选择出集群节点数量与集群容器的副本数量最相近的节点，提高了集群在部署容器时对节点的筛选效率；Through the first optimization algorithm, the node whose number of cluster nodes is most similar to the number of replicas of cluster containers is selected, which improves the screening efficiency of nodes when the cluster deploys containers;

优选算法二是根据集群剩余的CPU和内存的平均值对集群进行积分计算；The second preferred algorithm is to perform an integral calculation on the cluster according to the average value of the remaining CPU and memory of the cluster;

其中，集群所剩余的CPU和内存的平均值越大，集群的所得积分越高；反之，集群所剩余的CPU和内存的平均值越小，集群的所得积分越低。Among them, the larger the average value of the remaining CPU and memory of the cluster, the higher the score of the cluster; on the contrary, the smaller the average value of the remaining CPU and memory of the cluster, the lower the score of the cluster.

作为优选，根据集群部署列表和集群部署策略中对集群的需要，依次从集群部署列表中选择集群进行容器的部署具体如下：Preferably, according to the cluster deployment list and the cluster deployment strategy, select clusters from the cluster deployment list in order to deploy the container as follows:

通过集群容器调度单元解析集群容器的部署策略，解析出容器部署需要的集群部署信息，通过集群的部署信息依次从集群部署列表中获取集群进行容器部署。The deployment strategy of the cluster container is parsed by the cluster container scheduling unit, the cluster deployment information required for container deployment is parsed, and the clusters are sequentially obtained from the cluster deployment list through the cluster deployment information for container deployment.

一种电子设备，包括：存储器和至少一个处理器；An electronic device comprising: a memory and at least one processor;

其中，所述存储器上存储有计算机程序；Wherein, a computer program is stored on the memory;

所述至少一个处理器执行所述存储器存储的计算机程序，使得所述至少一个处理器执行如上述的基于kubernets多集群的容器调度方法。The at least one processor executes the computer program stored in the memory, so that the at least one processor executes the above-mentioned container scheduling method based on kubernets multi-cluster.

一种计算机可读存储介质，所述计算机可读存储介质中存储有计算机程序，所述计算机程序可被处理器执行以实现如上述的基于kubernets多集群的容器调度方法。A computer-readable storage medium stores a computer program in the computer-readable storage medium, and the computer program can be executed by a processor to implement the above-mentioned multi-cluster-based container scheduling method for kubernets.

本发明的基于kubernets多集群的容器调度系统及方法具有以下优点：The container scheduling system and method based on kubernets multi-cluster of the present invention have the following advantages:

(一)本发明通过集群预算算法和集群优选算法能够自动化的选择适合容器部署的集群列表，解决了目前kubernets多集群运管平台容器在多集群中自动化部署调度的问题，降低了工作人员的技术难度和工作成本；(1) The present invention can automatically select a cluster list suitable for container deployment through the cluster budget algorithm and the cluster optimization algorithm, solves the current problem of automatic deployment and scheduling of containers in the multi-cluster of the kubernets multi-cluster operation and management platform, and reduces the technical skills of the staff. Difficulty and cost of work;

(二)本发明通过合理的设置预算算法中的算法优先级可以大幅度的提高集群的筛选速度，提高了容器在多集群下的部署效率。(2) The present invention can greatly improve the screening speed of the cluster by reasonably setting the algorithm priority in the budget algorithm, and improve the deployment efficiency of the container in the multi-cluster.

附图说明Description of drawings

下面结合附图对本发明进一步说明。The present invention will be further described below with reference to the accompanying drawings.

附图1为基于kubernets多集群的容器调度方法的流程框图；Accompanying drawing 1 is the flow chart of the container scheduling method based on kubernets multi-cluster;

附图2为集群容器调度单元的工作过程的流程框图。FIG. 2 is a flow chart of the working process of the cluster container scheduling unit.

具体实施方式Detailed ways

参照说明书附图和具体实施例对本发明的基于kubernets多集群的容器调度系统及方法作以下详细地说明。The container scheduling system and method based on kubernets multi-cluster of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

实施例1：Example 1:

本实施例提供了一种基于kubernets多集群的容器调度系统，该系统包括集群纳管单元、集群信息检测单元、集群容器调度单元和存储单元；This embodiment provides a container scheduling system based on kubernets multi-cluster, the system includes a cluster management unit, a cluster information detection unit, a cluster container scheduling unit and a storage unit;

其中，集群纳管单元用于对不同环境下的kubernets集群进行统一的纳管，并把集群的状态信息存放到存储单元；Among them, the cluster management unit is used to uniformly manage the kubernets clusters in different environments, and store the status information of the cluster in the storage unit;

集群信息检测单元用于实时检测和收集被集群纳管单元纳管的kubernets集群的状态信息；The cluster information detection unit is used to detect and collect the status information of the kubernets cluster managed by the cluster management unit in real time;

集群容器调度单元用于解析集群容器部署策略的部署信息，分别使用集群预选算法和集群优先算法对集群进行筛选和积分计算；并根据集群容器部署策略对集群的需要进行容器在多集群中的自动化调度部署；The cluster container scheduling unit is used to analyze the deployment information of the cluster container deployment strategy, and use the cluster pre-selection algorithm and the cluster priority algorithm to filter and integrate the cluster respectively; and according to the cluster container deployment strategy, the cluster is required to automate containers in multiple clusters scheduling deployment;

存储单元用于存储集群容器部署策略、集群状态等需要存储的相关信息。The storage unit is used to store related information that needs to be stored, such as cluster container deployment policies and cluster status.

如附图2所示，本实施例中的集群容器调度单元的工作过程具体如下：As shown in FIG. 2 , the working process of the cluster container scheduling unit in this embodiment is as follows:

(1)、解析集群容器的部署策略；(1), analyze the deployment strategy of cluster containers;

(2)、判断是否存在多种集群预选信息：(2), determine whether there is a variety of cluster pre-selection information:

①、若是，则集群优选算法根据预选信息筛选集群，并跳转至步骤(4)；1. If yes, the cluster optimization algorithm filters the clusters according to the pre-selection information, and jumps to step (4);

②、若否，则执行步骤(3)；②, if not, execute step (3);

(3)、判断是否设置优先级字段：(3), determine whether to set the priority field:

①、若是，则使用设置的优先级筛选集群，并跳转至步骤(4)；①. If yes, use the set priority to filter the cluster and jump to step (4);

②、若否，则使用默认优先级和信息筛选集群，并跳转至步骤(4)；②. If not, use the default priority and information to filter the cluster, and jump to step (4);

(4)、获取可用集群列表；(4), get the list of available clusters;

(5)、对可用集群列表中的集群进行积分计算，公式如下：(5) Integrate the clusters in the list of available clusters, and the formula is as follows:

集群最终得分＝优选算法一得分*优选算法一权重+优选算法二得分*优选算法二权重；The final score of the cluster = the score of the optimal algorithm 1 * the weight of the optimal algorithm 1 + the score of the optimal algorithm 2 * the weight of the optimal algorithm 2;

其中，优选算法一是根据集群容器部署策略中容器在集群中的部署副本数和可用列表集群中集群的节点数量计算得分；具体如下：Among them, the first optimal algorithm is to calculate the score according to the number of deployed copies of the container in the cluster and the number of nodes in the cluster in the available list cluster in the cluster container deployment strategy; the details are as follows:

当集群的节点数量大于集群容器的副本数量时，集群节点数量越多，集群的得分越低；When the number of nodes in the cluster is greater than the number of replicas in the cluster container, the higher the number of cluster nodes, the lower the score of the cluster;

当集群的节点数量小于集群容器的副本数量时，集群的节点数量越少，集群得分越低；When the number of nodes in the cluster is less than the number of replicas in the cluster container, the fewer the number of nodes in the cluster, the lower the cluster score;

集群的节点数量小于集群容器副本数量的集群得分均小于集群节点数量大于容器副本数量的集群得分；The scores of clusters whose number of nodes is less than the number of cluster container replicas are all lower than those of clusters whose number of cluster nodes is greater than the number of container replicas;

通过优选算法一选择出集群节点数量与集群容器的副本数量最相近的节点，提高了集群在部署容器时对节点的筛选效率；Through the first optimization algorithm, the node whose number of cluster nodes is most similar to the number of replicas of cluster containers is selected, which improves the screening efficiency of nodes when the cluster deploys containers;

优选算法二是根据集群剩余的CPU和内存的平均值对集群进行积分计算；The second preferred algorithm is to perform an integral calculation on the cluster according to the average value of the remaining CPU and memory of the cluster;

其中，集群所剩余的CPU和内存的平均值越大，集群的所得积分越高；反之，集群所剩余的CPU和内存的平均值越小，集群的所得积分越低；Among them, the larger the average value of the remaining CPU and memory of the cluster, the higher the score of the cluster; on the contrary, the smaller the average value of the remaining CPU and memory of the cluster, the lower the score of the cluster;

(6)、通过集群积分计算公式、优选算法一得分和优选算法二得分计算出集群的最终得分；(6), calculate the final score of the cluster through the cluster integral calculation formula, the first score of the preferred algorithm and the second score of the preferred algorithm;

(7)、根据每个集群的最终得分对可用集群列表中的集群进行排序，得到集群部署列表。(7) Sort the clusters in the available cluster list according to the final score of each cluster to obtain a cluster deployment list.

实施例2：Example 2:

如附图1所示，本发明提供了一种基于kubernets多集群的容器调度方法，该方法具体如下：As shown in FIG. 1, the present invention provides a container scheduling method based on kubernets multi-cluster, the method is as follows:

S1、根据集群信息检测单元获取一个或多个可调度的集群，得到可调度的集群列表；S1. Acquire one or more schedulable clusters according to the cluster information detection unit, and obtain a list of schedulable clusters;

S2、通过集群预选算法对可调度的集群列表进行筛选，得到能够部署容器的集群列表为可用集群列表；S2. Screen the list of schedulable clusters through a cluster preselection algorithm, and obtain a list of clusters that can deploy containers as a list of available clusters;

S3、通过集群优选算法对可用集群列表中的集群进行积分计算，根据集群所得积分，对集群进行排序，得到集群部署列表；S3. Perform integral calculation on the clusters in the list of available clusters through the cluster optimization algorithm, and sort the clusters according to the scores obtained by the clusters to obtain a cluster deployment list;

S4、根据集群部署列表和集群部署策略中对集群的需要，依次从集群部署列表中选择集群进行容器的部署。S4. According to the cluster deployment list and the cluster deployment strategy, the clusters are sequentially selected from the cluster deployment list to deploy the container.

本实施例步骤S1中的可调度的集群列表的获取过程具体如下：The process of acquiring the schedulable cluster list in step S1 of this embodiment is as follows:

实时的监测已经被集群纳管单元纳管的集群的状态信息；具体如下：Real-time monitoring of the status information of the clusters that have been managed by the cluster management unit; the details are as follows:

①、若集群的状态信息为健康状态，则为可调度集群；①. If the status information of the cluster is healthy, it is a schedulable cluster;

②、若集群的状态信息为非健康状态，则为不可调度集群。②. If the status information of the cluster is not healthy, it is an unschedulable cluster.

本实施例步骤S2中的通过集群预选算法对可调度的集群列表进行筛选，得到能够部署容器的集群列表为可用集群列表具体如下：In step S2 of this embodiment, the list of schedulable clusters is filtered through the cluster preselection algorithm, and the list of clusters that can deploy containers is obtained as the list of available clusters. The details are as follows:

S201、通过集群容器调度单元解析集群容器的部署策略，根据集群容器部署策略选择合适的集群预选算法对可调度集群列表进行筛选工作得到可用集群列表；S201. Analyze the deployment strategy of the cluster container by the cluster container scheduling unit, and select an appropriate cluster preselection algorithm according to the cluster container deployment strategy to filter the list of schedulable clusters to obtain a list of available clusters;

S202、集群预选算法根据集群容器部署策略中所设置的集群名称、集群标签和集群分组信息对可调度集群列表进行筛选；S202, the cluster preselection algorithm filters the list of schedulable clusters according to the cluster name, cluster label and cluster grouping information set in the cluster container deployment policy;

S203、集群容器部署策略中所设置的集群名称、集群标签和集群分组信息均设置多条，例如：可以设置两条集群名称；同时集群容器部署策略中所设置的集群名称、集群标签和集群分组信息单独使用或组合使用；在组合使用时，筛选符合所有条件的集群，并通过算法优先级字段设置集群名称、集群标签和集群分组信息在集群预选算法中执行的优先级，通过合理的设置算法优先级有效的提高集群预选算法的执行效率；S203. Multiple pieces of cluster name, cluster label and cluster group information set in the cluster container deployment strategy are set, for example, two cluster names can be set; at the same time, the cluster name, cluster label and cluster group set in the cluster container deployment strategy The information is used alone or in combination; when used in combination, filter clusters that meet all the conditions, and set the cluster name, cluster label, and cluster grouping information in the cluster preselection algorithm through the algorithm priority field. The priority effectively improves the execution efficiency of the cluster preselection algorithm;

S204、所有通过集群预选算法的集群形成可用集群列表。S204. All clusters that have passed the cluster preselection algorithm form a list of available clusters.

本实施例步骤S3中的通过集群优选算法对可用集群列表中的集群进行积分计算，根据集群所得积分，对集群进行排序，得到集群部署列表具体如下：In step S3 of this embodiment, the clusters in the available cluster list are calculated by the cluster optimization algorithm, and the clusters are sorted according to the points obtained by the clusters, and the cluster deployment list is obtained as follows:

S301、对可用集群列表中的集群进行积分计算，公式如下：S301. Perform integral calculation on the clusters in the available cluster list, and the formula is as follows:

集群最终得分＝优选算法一得分*优选算法一权重+优选算法二得分*优选算法二权重；The final score of the cluster = the score of the optimal algorithm 1 * the weight of the optimal algorithm 1 + the score of the optimal algorithm 2 * the weight of the optimal algorithm 2;

S302、通过集群积分计算公式、优选算法一得分和优选算法二得分计算出集群的最终得分，根据每个集群的最终得分对可用集群列表中的集群进行排序，得到集群部署列表。S302. Calculate the final score of the cluster through the cluster integral calculation formula, the score of the first optimization algorithm and the score of the second optimization algorithm, and sort the clusters in the available cluster list according to the final score of each cluster to obtain a cluster deployment list.

本实施例中的优选算法一是根据集群容器部署策略中容器在集群中的部署副本数和可用列表集群中集群的节点数量计算得分；具体如下：The first preferred algorithm in this embodiment is to calculate the score according to the number of deployed copies of the container in the cluster and the number of nodes in the cluster in the available list cluster in the cluster container deployment policy; the details are as follows:

当集群的节点数量大于集群容器的副本数量时，集群节点数量越多，集群的得分越低；When the number of nodes in the cluster is greater than the number of replicas in the cluster container, the higher the number of cluster nodes, the lower the score of the cluster;

当集群的节点数量小于集群容器的副本数量时，集群的节点数量越少，集群得分越低；When the number of nodes in the cluster is less than the number of replicas in the cluster container, the fewer the number of nodes in the cluster, the lower the cluster score;

集群的节点数量小于集群容器副本数量的集群得分均小于集群节点数量大于容器副本数量的集群得分；The scores of clusters whose number of nodes is less than the number of cluster container replicas are all lower than those of clusters whose number of cluster nodes is greater than the number of container replicas;

通过优选算法一选择出集群节点数量与集群容器的副本数量最相近的节点，提高了集群在部署容器时对节点的筛选效率；Through the first optimization algorithm, the node whose number of cluster nodes is most similar to the number of replicas of cluster containers is selected, which improves the screening efficiency of nodes when the cluster deploys containers;

本实施例中的优选算法二是根据集群剩余的CPU和内存的平均值对集群进行积分计算；The second preferred algorithm in this embodiment is to perform an integral calculation on the cluster according to the average value of the remaining CPU and memory of the cluster;

其中，集群所剩余的CPU和内存的平均值越大，集群的所得积分越高；反之，集群所剩余的CPU和内存的平均值越小，集群的所得积分越低。Among them, the larger the average value of the remaining CPU and memory of the cluster, the higher the score of the cluster; on the contrary, the smaller the average value of the remaining CPU and memory of the cluster, the lower the score of the cluster.

本实施例步骤S4中的根据集群部署列表和集群部署策略中对集群的需要，依次从集群部署列表中选择集群进行容器的部署具体如下：In step S4 of this embodiment, according to the cluster deployment list and the cluster deployment strategy, selecting clusters from the cluster deployment list in turn to deploy the container is as follows:

通过集群容器调度单元解析集群容器的部署策略，解析出容器部署需要的集群部署信息，通过集群的部署信息依次从集群部署列表中获取集群进行容器部署。The deployment strategy of the cluster container is parsed by the cluster container scheduling unit, the cluster deployment information required for container deployment is parsed, and the clusters are sequentially obtained from the cluster deployment list through the cluster deployment information for container deployment.

实施例3：Example 3:

本实施例还提供了一种电子设备，包括：存储器和处理器；This embodiment also provides an electronic device, including: a memory and a processor;

其中，存储器存储计算机执行指令；Wherein, the memory stores computer execution instructions;

处理器执行所述存储器存储的计算机执行指令，使得处理器执行本发明任一实施例中的基于kubernets多集群的容器调度方法。The processor executes the computer-executed instructions stored in the memory, so that the processor executes the container scheduling method based on kubernets multi-cluster in any embodiment of the present invention.

处理器可以是中央处理单元(CPU)，还可以是其他通用处理器、数字信号处理器(DSP)、专用集成电路(ASIC)、现成可编程门阵列(FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通过处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。The processor may be a central processing unit (CPU), but also other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs) or other programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, etc. The processor may be a microprocessor or the processor may be any conventional processor or the like.

存储器可用于储存计算机程序和/或模块，处理器通过运行或执行存储在存储器内的计算机程序和/或模块，以及调用存储在存储器内的数据，实现电子设备的各种功能。存储器可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序等；存储数据区可存储根据终端的使用所创建的数据等。此外，存储器还可以包括高速随机存取存储器，还可以包括非易失性存储器，例如硬盘、内存、插接式硬盘，只能存储卡(SMC)，安全数字(SD)卡，闪存卡、至少一个磁盘存储期间、闪存器件、或其他易失性固态存储器件。The memory can be used to store computer programs and/or modules, and the processor implements various functions of the electronic device by running or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory may mainly include a stored program area and a stored data area, wherein the stored program area may store an operating system, an application program required for at least one function, and the like; the stored data area may store data created according to the use of the terminal, and the like. In addition, the memory may also include high-speed random access memory, and may also include non-volatile memory such as hard disks, internal memory, plug-in hard disks, memory-only cards (SMC), secure digital (SD) cards, flash memory cards, at least A disk storage period, flash memory device, or other volatile solid state storage device.

实施例4：Example 4:

本实施例还提供了一种计算机可读存储介质，其中存储有多条指令，指令由处理器加载，使处理器执行本发明任一实施例中的基于kubernets多集群的容器调度方法。具体地，可以提供配有存储介质的系统或者装置，在该存储介质上存储着实现上述实施例中任一实施例的功能的软件程序代码，且使该系统或者装置的计算机(或CPU或MPU)读出并执行存储在存储介质中的程序代码。This embodiment also provides a computer-readable storage medium, in which a plurality of instructions are stored, and the instructions are loaded by the processor, so that the processor executes the container scheduling method based on kubernets multi-cluster in any embodiment of the present invention. Specifically, it is possible to provide a system or device equipped with a storage medium on which software program codes for implementing the functions of any of the above-described embodiments are stored, and which enables a computer (or CPU or MPU of the system or device) ) to read and execute the program code stored in the storage medium.

在这种情况下，从存储介质读取的程序代码本身可实现上述实施例中任何一项实施例的功能，因此程序代码和存储程序代码的存储介质构成了本发明的一部分。In this case, the program code itself read from the storage medium can implement the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code form part of the present invention.

用于提供程序代码的存储介质实施例包括软盘、硬盘、磁光盘、光盘(如CD-ROM、CD-R、CD-RW、DVD-ROM、DVD-RYM、DVD-RW、DVD+RW)、磁带、非易失性存储卡和ROM。可选择地，可以由通信网络从服务器计算机上下载程序代码。Examples of storage media for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (eg CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RYM, DVD-RW, DVD+RW), Magnetic tapes, non-volatile memory cards and ROMs. Alternatively, the program code may be downloaded from a server computer over a communications network.

此外，应该清楚的是，不仅可以通过执行计算机所读出的程序代码，而且可以通过基于程序代码的指令使计算机上操作的操作系统等来完成部分或者全部的实际操作，从而实现上述实施例中任意一项实施例的功能。In addition, it should be clear that part or all of the actual operations can be implemented not only by executing the program code read out by the computer, but also by the operating system or the like operating on the computer based on the instructions of the program code, so as to realize the above-mentioned embodiments. Function of any one of the embodiments.

此外，可以理解的是，将由存储介质读出的程序代码写到插入计算机内的扩展板中所设置的存储器中或者写到与计算机相连接的扩展单元中设置的存储器中，随后基于程序代码的指令使安装在扩展板或者扩展单元上的CPU等来执行部分和全部实际操作，从而实现上述实施例中任一实施例的功能。In addition, it can be understood that the program code read from the storage medium is written into the memory provided in the expansion board inserted into the computer or into the memory provided in the expansion unit connected to the computer, and then based on the program code The instructions cause the CPU or the like installed on the expansion board or the expansion unit to perform part and all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

最后应说明的是：以上各实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述各实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的范围。Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: The technical solutions described in the foregoing embodiments can still be modified, or some or all of the technical features thereof can be equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present invention. scope.

Claims (10)

1. A container scheduling system based on kubernets multi-cluster is characterized by comprising a cluster receiving and managing unit, a cluster information detection unit, a cluster container scheduling unit and a storage unit;
the cluster admission management unit is used for carrying out unified admission management on the kubernets in different environments and storing the state information of the clusters in the storage unit;
the cluster information detection unit is used for detecting and collecting the state information of the kubernets cluster managed by the cluster management unit in real time;
the cluster container scheduling unit is used for analyzing the deployment information of the cluster container deployment strategy, and respectively using a cluster preselection algorithm and a cluster priority algorithm to screen and calculate the integral of the cluster; carrying out automatic dispatching deployment of the containers in the multiple clusters according to the cluster container deployment strategy;
the storage unit is used for storing relevant information needing to be stored, such as a cluster container deployment strategy, a cluster state and the like.
2. The system according to claim 1, wherein the cluster container scheduling unit is configured to perform the following operations:
(1) Analyzing the deployment strategy of the cluster container;
(2) Judging whether various cluster preselection information exists:
(1) if so, screening the cluster according to the preselected information by the cluster optimization algorithm, and skipping to the step (4);
(2) if not, executing the step (3);
(3) Judging whether a priority field is set:
(1) if so, screening the cluster by using the set priority, and skipping to the step (4);
(2) if not, using the default priority and the information screening cluster, and skipping to the step (4);
(4) Acquiring an available cluster list;
(5) And carrying out integral calculation on the clusters in the available cluster list, wherein the formula is as follows:
cluster end score = preferred algorithm one score + preferred algorithm two weight;
calculating a score according to the number of deployment copies of the container in the cluster container deployment strategy and the number of nodes of the cluster in the available list cluster; the method comprises the following specific steps:
when the number of the nodes of the cluster is larger than the number of the copies of the cluster container, the more the number of the nodes of the cluster is, the lower the score of the cluster is;
when the number of the nodes of the cluster is smaller than the number of the copies of the cluster container, the smaller the number of the nodes of the cluster is, the lower the cluster score is;
the cluster scores of the cluster with the node number smaller than the container copy number of the cluster are smaller than the cluster scores with the node number larger than the container copy number of the cluster;
selecting a node with the cluster node number closest to the copy number of the cluster container through a first preferred algorithm;
the second preferred algorithm is to perform integral calculation on the cluster according to the average values of the rest CPUs and the memories of the cluster;
wherein, the larger the average value of the residual CPU and the memory of the cluster is, the higher the obtained integral of the cluster is; conversely, the smaller the average value of the remaining CPUs and memories of the cluster is, the lower the obtained integral of the cluster is;
(6) Calculating the final score of the cluster through a cluster integral calculation formula, the score of the preferred algorithm I and the score of the preferred algorithm II;
(7) And sorting the clusters in the available cluster list according to the final score of each cluster to obtain a cluster deployment list.
3. A container scheduling method based on a kubernets multi-cluster is characterized by comprising the following steps:
acquiring one or more schedulable clusters according to the cluster information detection unit to obtain a schedulable cluster list;
screening the schedulable cluster list through a cluster preselection algorithm to obtain a cluster list capable of deploying a container as an available cluster list;
performing integral calculation on the clusters in the available cluster list through a cluster optimization algorithm, and sequencing the clusters according to the integral obtained by the clusters to obtain a cluster deployment list;
and sequentially selecting the clusters from the cluster deployment list to deploy the containers according to the cluster deployment list and the needs of the clusters in the cluster deployment strategy.
4. The method for container scheduling based on kubernets multi-cluster according to claim 3, wherein the obtaining process of the schedulable cluster list is specifically as follows:
monitoring the state information of the cluster which is managed by the cluster managing unit in real time; the method comprises the following specific steps:
if the state information of the cluster is a healthy state, the cluster is a schedulable cluster;
and if the state information of the cluster is in the unhealthy state, the cluster is not dispatchable.
5. The method for container scheduling based on kubernets multiple clusters according to claim 3, wherein the cluster list that can be scheduled is obtained by screening the schedulable cluster list through a cluster preselection algorithm, and the cluster list that can deploy the containers is the available cluster list as follows:
analyzing a deployment strategy of the cluster container through a cluster container scheduling unit, and selecting a proper cluster preselection algorithm according to the cluster container deployment strategy to screen a schedulable cluster list to obtain an available cluster list;
the cluster preselection algorithm screens the schedulable cluster list according to the cluster name, the cluster label and the cluster grouping information set in the cluster container deployment strategy;
a plurality of cluster names, cluster labels and cluster grouping information set in the cluster container deployment strategy are set; meanwhile, the cluster name, the cluster label and the cluster grouping information set in the cluster container deployment strategy are used independently or in combination; when the cluster pre-selection algorithm is used in a combined mode, the clusters which accord with all conditions are screened, and the execution priority of the cluster names, the cluster labels and the cluster grouping information in the cluster pre-selection algorithm is set through the algorithm priority field;
all clusters that pass the cluster pre-selection algorithm form a list of available clusters.
6. The method for container scheduling based on kubernets multiple clusters according to any one of claims 3-5, wherein the clusters in the available cluster list are subjected to integral calculation through a cluster preference algorithm, and the clusters are sorted according to the integral obtained by the clusters, so as to obtain a cluster deployment list as follows:
performing integral calculation on the clusters in the available cluster list, wherein the formula is as follows:
cluster end score = preferred algorithm one score + preferred algorithm two weight;
and calculating the final scores of the clusters through a cluster integral calculation formula, the first preferred algorithm score and the second preferred algorithm score, and sequencing the clusters in the available cluster list according to the final scores of all the clusters to obtain a cluster deployment list.
7. The method for container scheduling based on kubernets multi-cluster according to claim 6, wherein the preferred algorithm is to calculate scores according to the deployment copy number of the container in the cluster container deployment strategy and the node number of the cluster in the available list cluster; the method comprises the following specific steps:
when the number of the nodes of the cluster is larger than the number of the copies of the cluster container, the more the number of the nodes of the cluster is, the lower the score of the cluster is;
when the number of the nodes of the cluster is smaller than the number of the copies of the cluster container, the smaller the number of the nodes of the cluster is, the lower the cluster score is;
the cluster scores of the cluster with the node number smaller than the container copy number of the cluster are smaller than the cluster scores with the node number larger than the container copy number of the cluster;
selecting the nodes with the cluster node number which is most similar to the copy number of the cluster container through a preferred algorithm I;
the second preferred algorithm is to perform integral calculation on the cluster according to the average values of the rest CPUs and the memories of the cluster;
wherein, the larger the average value of the remaining CPU and memory of the cluster is, the higher the obtained integral of the cluster is; conversely, the smaller the average of the remaining CPU and memory of the cluster, the lower the resulting integral of the cluster.
8. The method for container scheduling based on kubernets multiple clusters according to claim 3, wherein the method for container deployment sequentially selects clusters from the cluster deployment list according to the cluster deployment list and the cluster deployment policy for the needs of the clusters specifically comprises the following steps:
and analyzing the deployment strategy of the cluster container through the cluster container scheduling unit, analyzing cluster deployment information required by container deployment, and sequentially acquiring the cluster from the cluster deployment list through the deployment information of the cluster to deploy the container.
9. An electronic device, comprising: a memory and at least one processor;
wherein the memory has stored thereon a computer program;
the at least one processor executing the memory-stored computer program to cause the at least one processor to perform the method of kubernets multi-cluster based container scheduling of any of claims 3-8.
10. A computer-readable storage medium, having stored thereon a computer program executable by a processor for implementing the method for kubernets multi-cluster based container scheduling according to any of claims 3 to 8.
CN202210797835.9A 2022-07-08 2022-07-08 Container scheduling system and method based on kubernets multi-cluster Pending CN115145691A (en)

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