郑州大学学报（理学版）2026，Vol.58Issue(2)：33-39,47,8.DOI:10.13705/j.issn.1671-6841.2024139
基于强化学习的多目标微服务部署方法
A Reinforcement Learning Based Approach to Multi-objective Microservice Deployment
摘要
Abstract
In edge computing,microservice architecture could improve data processing efficiency and ap-plication response speed,which was suitable for various application scenarios with fast response and fre-quent interactions.However,existing studies neglected the impact of different interaction frequencies be-tween microservices on the communication overhead.To address this problem,a multi-objective microser-vice optimal deployment method based on reinforcement learning to improve the performance of microser-vices in edge environments was proposed.A dual optimization objective model that considers reducing the communication overhead of microservice interactions and balancing the resources of edge nodes was estab-lished.Then a deep Q-learning algorithm based on an improved reward mechanism was designed.In or-der to adapt to the characteristics of shared resources in the process of microservice deployment,a shared reward mechanism was introduced so that the algorithm had better convergence.The experimental results showed that the proposed algorithm could balance the microservice interaction perception and node re-source utilization better,and had shorter response time compared with the existing DIM method and Ku-bernetes default deployment method.关键词
边缘计算/微服务框架/微服务部署/强化学习Key words
edge computing/microservice framework/microservice deployment/reinforced learning分类
信息技术与安全科学引用本文复制引用
张璊瑶,张盈希,郑文祺,冯光升..基于强化学习的多目标微服务部署方法[J].郑州大学学报（理学版）,2026,58(2):33-39,47,8.基金项目
群集适应性系统建模与宏观行为分析方法研究基金项目(62272126) （62272126）
