计算机科学 ›› 2023, Vol. 50 ›› Issue (4): 233-240.doi: 10.11896/jsjkx.220300215
谢雍生1, 黄相恒1, 陈宁江1,2,3
XIE Yongsheng1, HUANG Xiangheng1, CHEN Ningjiang1,2,3
摘要： 容器云系统的资源调度策略对资源利用率和集群性能起着重要作用。现有的容器集群调度没有充分考虑节点内部和节点之间的资源占用情况,容易出现容器资源瓶颈,造成资源利用率低和服务可靠性差的问题。为了均衡容器集群的工作负载,减少容器资源瓶颈的出现,提出了一种基于DQN(Deep Q-learning Network)的容器集群调度优化算法CS-DQN(Container Scheduling Optimization Strategy Based on DQN)。首先提出一种面向负载均衡的容器集群资源利用率优化模型。然后利用深度强化学习方法,设计一种基于DQN的容器集群调度算法,定义相关的状态空间、动作空间和奖励函数。通过引入改进的DQN算法,基于自学习方法生成满足优化目标的容器动态调度策略。实验结果表明,该调度策略扩大了在调度中可部署容器的规模,在不同的工作负载中实现了较好的负载均衡,提高了资源利用率,更好地保证了服务可靠性。
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