Intelligent Orchestrating of IoT Microservices Based on Reinforcement Learning

作     者：WU Yuqin SHEN Congqi CHEN Shuhan WU Chunming LI Shunbin WEI Ruan WU Yuqin;SHEN Congqi;CHEN Shuhan;WU Chunming;LI Shunbin;WEI Ruan

作者机构：College of Computer Science and Technology Zhejiang University College of Information and Electrical Engineering Ningde Normal University Zhejiang Lab College of Control Science and Engineering Zhejiang University 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2022年第31卷第5期

页      面：930-937页

核心收录：

学科分类：08[工学] 081104[工学-模式识别与智能系统] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Key Research and Development Project (2018YFB2100404) the key R&D Program of Zhejiang Province (2020C01077) Fujian Natural Science Foundation (2020J01431) Ningde Normal University Innovation Team Program (2018T04) the Fundamental Research Funds for the Central Universities(Zhejiang University NGICS Platform) the Major Scientific Project of Zhejiang Lab (2018FD0ZX01) 

主　　题：Internet of things Microservice Traffc scheduling Reinforcement learning 

摘      要：With the recent increase in the number of Internet of things(IoT) services, an intelligent scheduling strategy is needed to manage these services. In this paper, the problem of automatic choreography of microservices in IoT is explored. A type of reinforcement learning(RL) algorithm called TD3 is used to generate the optimal choreography policy under the framework of a softwaredefined network. The optimal policy is gradually reached during the learning procedure to achieve the goal, despite the dynamic characteristics of the network environment. The simulation results show that compared with other methods, the TD3 algorithm converges faster after a certain number of iterations, and it performs better than other non-RL algorithms by obtaining the highest reward. The TD3 algorithm can effciently adjust the traffic transmission path and provide qualified IoT services.