云边环境下微服务组合系统的动态演化方法  

Dynamic evolution method for microservice composition systems in cloud-edge environment

作　　者：叶盛[1,2] 王菁[1,2] 辛建峰 王桂玲[1,2] 郭陈虹 YE Sheng;WANG Jing;XIN Jianfeng;WANG Guiling;GUO Chenhong(School of Information Science and Technology North China University of Technology,Beijing 100144,China;Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data(North China University of Technology),Beijing 100144,China;China Cybersecurity Review Technology and Certificate Center,Beijing 100013,China)

机构地区：[1]北方工业大学信息学院,北京100144 [2]大规模流数据集成与分析技术北京市重点实验室(北方工业大学),北京100144 [3]中国网络安全审查技术与认证中心,北京100013

出　　处：《计算机应用》2023年第6期1696-1704,共9页journal of Computer Applications

基　　金：国家重点研发计划项目(2018YFB1402500);国家自然科学基金国际(地区)合作与交流项目(62061136006);国家自然科学基金重点项目(61832004)。

摘　　要：针对云边环境下用户需求不确定导致微服务组合逻辑会随着用户需求的变化而动态调整的问题,提出了云边环境下的微服务组合系统的动态演化方法(DE4MC)。首先,自动识别用户的操作并进行相应的算法策略;其次,在部署阶段,用户提交业务流程之后,系统通过所提方法中的部署算法选择较优的节点进行部署;最后,在动态调整阶段,用户调整业务流程实例后,系统通过所提方法中的动态调整算法进行动态演化。所提方法中的两个算法均综合考虑微服务实例的迁移代价、微服务与用户的数据通信代价和微服务之间的数据流传输代价以选择较优的节点进行部署,从而缩短了运行时间,降低了演化开销。在仿真实验中,在部署阶段,所提方法的部署算法与启发式算法(HA)+二代非支配排序遗传算法(NSGA-Ⅱ)的算法组合相比,各个规模的平均运行时间缩短了9.7%,演化总开销降低了16.8%;在动态调整阶段,所提方法的动态调整算法与HA+NSGA-Ⅱ的算法组合相比,各个规模平均运行时间缩短了6.3%,演化总开销降低了21.7%。实验结果表明,所提方法保证了云边环境下微服务组合系统能在演化开销低和业务流程时间短的条件下及时演化,并且能够提供用户满意的服务质量。As the uncertainty of user requirements in the cloud-edge environment causes the microservice composition logic to be dynamically adjusted with the changes of user needs,a Dynamic Evolution method for Microservice Composition system(DE4MC)in the cloud-edge environment was proposed.Firstly,the user􀆳s operation was automatically recognized to implement the corresponding algorithm strategy.Secondly,in the deployment stage,the better node was selected by the system for deployment through the deployment algorithm in the proposed method after the user submitting the business process.Finally,in the dynamic adjustment stage,the dynamic evolution was performed by the system through the dynamic adjustment algorithm in the proposed method after the user adjusting the business process instances.In both algorithms in the proposed method,the migration cost of microservice instances,the data communication cost between microservices and users,and the data flow transmission cost between microservices were comprehensively considered to select better nodes for deployment,which shortened the running time and reduced the evolution cost.In the simulation experiment,in the deployment stage,the deployment algorithm in the proposed method has average running time of all scales 9.7%lower and total evolution cost 16.8%lower than those of the combination algorithm of Heuristic Algorithm(HA)with Non-dominated Sorting Genetic Algorithm-Ⅱ(NSGA-Ⅱ);in the dynamic adjustment stage,compared with the combination algorithm of HA and NSGA-Ⅱ,the dynamic adjustment algorithm in the proposed method has the average running time of all scales 6.3%lower,and the total evolution cost 21.7%lower.Experimental results show that the proposed method ensures timely evolution of the microservice composition system in the cloud-edge environment with low evolution cost and short business process time,and provides users with satisfactory quality of service.

关 键 词：云边协同 业务流程 微服务组合 微服务调度 微服务演化 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]