计算机技术与发展2024，Vol.34Issue(9)：16-22,7.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0163
边缘场景下基于DDQN的容器组调度策略
Container Group Scheduling Optimization Strategy Based on DDQN in Edge Scenarios
摘要
Abstract
The industrial Internet is populated with a large number of on/offline container services deployed on edge servers.On the on hand,these container services bear the demand for low latency and high response,and on the other hand,they have intricate invocation re-lationships.The usual scheduling strategies for edge clusters do not take into account the dependencies between container services,leading to dependent container services possibly being dispersed across different edge nodes during scheduling,thereby generating a large number of cross-node calls and causing additional resource loss.We propose an optimization strategy for container group scheduling in edge scenarios for containers with dependencies.Firstly,the CDSC(Container Dependency Spectral Clustering)is used to divide dependent containers into one or more container groups,maximizing the dependency strength within groups and minimizing it between groups,to reduce the frequency of cross-node calls.Then,by introducing the Double Deep Q-Network model(Double DQN),the container group is used as the basic scheduling unit,with container dependency overhead,cluster and intra-node load as optimization targets.The strategy adaptively learns and optimizes scheduling strategies according to the actual situation of edge nodes,enabling it to cope with complex and changing edge cluster situations.Experimental results show that compared to traditional heuristic algorithms and deep reinforcement learning algorithms,the proposed algorithm has significant advantages in terms of container service response time,cluster and node load.关键词
调度优化/深度强化学习/容器聚类/集群/容器依赖开销Key words
scheduling optimization/deep reinforcement learning/container clustering/cluster/container dependency overhead分类
信息技术与安全科学引用本文复制引用
王钰童,顾进广..边缘场景下基于DDQN的容器组调度策略[J].计算机技术与发展,2024,34(9):16-22,7.基金项目
武汉市重点研发计划(2022012202015070) （2022012202015070）
