基于组合权重TOPSIS的Kubernetes调度算法

Kubernetes Scheduling Algorithm of TOPSIS Based on Combined Weight

Kubernetes是比较流行的开源容器编排引擎,其默认调度算法只考虑了CPU和内存两种性能指标,且采用统一权重计算候选节点得分,无法满足各异的Pod应用需求.本文在此基础上扩展了Kubernetes性能指标,增加了带宽、磁盘、IO速率3种指标,并通过AHP (analytic hierarchy process,层次分析法)计算主观权重和EW (entropy weight,熵权法)根据Pod应用部署过程中节点的性能指标的资源利用率实时计算资源指标的客观权重.两种权重相结合应用到改进的TOPSIS (technique for order preference by similarity to an ideal solution,逼近理想解排序方法)多属性决策方法中来选择合适的候选节点.实验结果表明,随着部署Pod数量的增多,在集群负载较大的情况下,综合负载的标准差和Kubernetes默认调度算法相比提升18%.

Kubernetes is a popular open-source container orchestration engine. Its default scheduling algorithm only considers CPU and memory and uses unified weight to calculate the score of candidate nodes, which cannot meet the requirements of different Pod applications. In view of this, the paper expands the Kubernetes performance indexes, with bandwidth, disk capacity, and IO rate added. The subjective weight is calculated by the analytic hierarchy process(AHP)and the objective weight of resource indexes is calculated by the entropy weight(EW) method in real time according to the resource utilization rate of performance indexes of nodes in the Pod application deployment process. We combine the two weights and apply them to a multi-attribute decision algorithm based on the improved technique for order preference by similarity to an ideal solution(TOPSIS) to select appropriate candidate nodes. The experiment results show that with the increase in the deployed Pod number, the standard deviation of the integrated load increases by 18% compared with that of the Kubernetes default scheduling algorithm under the condition of a large cluster load.