高技术通讯2026，Vol.36Issue(3)：268-278,11.DOI:10.3772/j.issn.1002-0470.2026.03.005
基于贝叶斯优化的微服务资源弹性分配机制
Bayesian optimization based auto-scaling of resource allocation mechanism for microservices
摘要
Abstract
Many latency-sensitive applications in data center networks adopt microservice architecture.To simultaneously meet the latency requirements of microservice applications while improving resource utilization,this paper proposes BScaler,a mechanism for elastic resource allocation in microservices.BScaler models the microservice resource al-location problem as a black-box function optimization problem and employs Bayesian optimization to determine the minimal resource allocation solution that satisfies latency objectives.To accelerate the Bayesian optimization process,a graph convolutional network(GCN)-based microservice latency prediction model is constructed to re-place the sampling process in Bayesian optimization.When workload fluctuates,BScaler generates resource config-uration plans for all microservices based on current load patterns,request types,and microservice topology,trigge-ring autoscaling actions to maintain service level objectives(SLO)while reducing resource waste.Extensive experi-mental results conducted in both simulation and real-world environments demonstrate that,compared to the widely-adopted Kubernetes HPA elastic scaling mechanism,BScaler reduces the number of microservice replicas by 3.9%～6.2%while meeting request latency SLOs.Compared to DScaler,a machine learning-based resource allocation mechanism,BScaler reduces the number of microservice replicas by 5.6%.关键词
微服务/资源管理/弹性伸缩/贝叶斯优化Key words
microservice/resource allocation/autoscaling/Bayesian optimization引用本文复制引用
张丁杰,武庆华,谢高岗..基于贝叶斯优化的微服务资源弹性分配机制[J].高技术通讯,2026,36(3):268-278,11.基金项目
国家重点研发计划(2022YFB2901800)资助项目. （2022YFB2901800）
