文献类型：期刊文献

英文题名：Multi-Objective Optimization of Container-Based Microservice Scheduling in Edge Computing

作者：Fan, Guisheng[1,2];Chen, Liang[1];Yu, Huiqun[1];Qi, Wei[1]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai, Peoples R China;[2]Shanghai Key Lab Comp Software Evaluating & Testi, Shanghai, Peoples R China

年份：2021

卷号：18

期号：1

起止页码：23

外文期刊名：COMPUTER SCIENCE AND INFORMATION SYSTEMS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000614630200003)】；

基金：This work was partially supported by the NSF of China under Grant nos. 61702334 and 61772200, Shanghai Municipal Natural Science Foundation under Grant nos. 17ZR1406900 and 17ZR1429700, Action Plan for Innovation on Science and Technology Projects of Shanghai under Grant no. 16511101000, Collaborative Innovation Foundation of Shanghai Institute of Technology under Grant no. XTCX2016-20, and Educational Research Fund of ECUST under Grant no. ZH1726108.

语种：英文

外文关键词：edge computing; microservice; container orchestration; multi-objective optimization; particle swarm optimization

摘要：Edge computing provides physical resources closer to end users, becoming a good complement to cloud computing. With the rapid development of container technology and microservice architecture, container orchestration has become a hot issue. However, the container-based microservice scheduling problem in edge computing is still urgent to be solved. In this paper, we first formulate the container-based microservice scheduling as a multi-objective optimization problem, aiming to optimize network latency among microservices, reliability of microservice applications and load balancing of the cluster. We further propose a latency, reliability and load balancing aware scheduling (LRLBAS) algorithm to determine the container-based microservice deployment in edge computing. Our proposed algorithm is based on particle swarm optimization (PSO). In addition, we give a handling strategy to separate the fitness function from constraints, so that each particle has two fitness values. In the proposed algorithm, a new particle comparison criterion is introduced and a certain proportion of infeasible particles are reserved adaptively. Extensive simulation experiments are conducted to demonstrate the effectiveness and efficiency of the proposed algorithm compared with other related algorithms.