摘要
Kubernetes在优选阶段仅根据节点CPU和内存的利用率来决定节点的分值,这只能保证单节点的资源利用率,无法保证集群资源的负载均衡.针对该问题,提出一种基于遗传算法的Kubernetes资源调度算法,该算法加入了网络带宽和磁盘IO两项评价指标,同时为评价指标赋予不同权重值,并且引入校验字典校验并修复遗传算法生成的新种群中不符合配置的个体.实验结果表明,与Kubernetes默认资源调度策略相比,该算法考虑了集群中的所有节点的资源利用率,在保证集群负载均衡方面有着更好的效果.
In the optimization stage,Kubernetes determines the score of a node only according to its utilization of CPU and memory.This can only guarantee the resource utilization of a single node but fails to achieve the load balancing of cluster resources.In response to this problem,a genetic algorithm-based Kubernetes resource scheduling algorithm is proposed.In the algorithm,two evaluation indicators,i.e.,network bandwidth and disk IO,are added and assigned with different weights.In addition,a check dictionary is introduced to check and repair the individuals that do not meet the configuration in the new population generated by the genetic algorithm.Experimental results show that compared with the Kubernetes default resource scheduling strategy,this algorithm takes into account the resource utilization of all nodes in the cluster and performs better in ensuring cluster load balancing.
作者
胡程鹏
薛涛
HU Cheng-Peng;XUE Tao(School of Computer Science,Xi’an Polytechnic University,Xi’an 710048,China)
出处
《计算机系统应用》
2021年第9期152-160,共9页
Computer Systems & Applications
基金
陕西省技术创新引导专项(2020CGXNG-012)。
作者简介
通讯作者:薛涛,E-mail:2843053453@qq.com。
