基于深度强化学习的微服务链动态负载均衡算法  

DYNAMIC LOAD BALANCING ALGORITHM OF MICROSERVICE CHAIN BASED ON DEEP REINFORCEMENT LEARNING

作　　者：章苏尧 Zhang Suyao(Software School of Fudan University,Shanghai 200438,China)

机构地区：[1]复旦大学软件学院,上海200438

出　　处：《计算机应用与软件》2025年第4期303-310,339,共9页Computer Applications and Software

基　　金：国家重点研发计划项目(2018YFB1703500)。

摘　　要：越来越多的云服务选择从单体架构转向微服务架构。在微服务架构下,请求会经过多个微服务,从而形成一条微服务链。多条微服务链之间存在资源竞争关系,单一微服务的阻塞可能影响任意相关微服务链并导致超时,从而违反用户请求的服务级别目标(Service Level Objectives,SLO)。提出一种融合服务网格与深度强化学习的微服务链动态负载均衡算法,可以在面对动态变化的负载时,尽最大可能不违反用户预期。实验结果表明,基于服务网格的微服务链拆分模块相较于已有方法取得了10倍以上的性能提升,而负载均衡算法比基准方法至少减少了46%的SLO违规。More and more cloud services are shifting from monolithic architecture to microservice architecture.Under the microservice architecture,requests will traverse multiple microservices to form a microservice chain.Multiple microservice chains may compete for resources,and the block of a single microservice may be transmitted to any related microservice chain and result in timeout,thus violating the service level objectives(SLO)requested by the user.This paper proposes a dynamic load balancing algorithm for microservice chains that integrates service mesh and deep reinforcement learning,which can satisfy user expectations as much as possible in the face of dynamically changing loads.Experimental results show that the performance of service-mesh based microservice chain split module is improved by more than 10 times compared with the existing methods,and the load balancing algorithm can reduce SLO violations by at least 46%comparedwith the benchmark method.

关 键 词：微服务 微服务链 负载均衡 深度强化学习 服务网格 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]