Multi-user reinforcement learning based task migration in mobile edge computing  

作　　者：Yuya CUI Degan ZHANG Jie ZHANG Ting ZHANG Lixiang CAO Lu CHEN 

机构地区：[1]Tianjin Key Lab of Intelligent Computing and Novel Software Technology,Tianjin University of Technology,Tianjin 300384,China [2]School of Internet of Things Engineering,Jiangsu Vocational College of Information Technology,Wuxi 214153,China [3]School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China [4]School of Sports Economics and Management,Tianjin University of Sport,Tianjin 301617,China

出　　处：《Frontiers of Computer Science》2024年第4期161-173,共13页中国计算机科学前沿（英文版）

基　　金：Basic Science(Natural Science)Research Project of Colleges and universities in Jiangsu Province(22KJB520017).

摘　　要：Mobile Edge Computing(MEC)is a promising approach.Dynamic service migration is a key technology in MEC.In order to maintain the continuity of services in a dynamic environment,mobile users need to migrate tasks between multiple servers in real time.Due to the uncertainty of movement,frequent migration will increase delays and costs and non-migration will lead to service interruption.Therefore,it is very challenging to design an optimal migration strategy.In this paper,we investigate the multi-user task migration problem in a dynamic environment and minimizes the average service delay while meeting the migration cost.In order to optimize the service delay and migration cost,we propose an adaptive weight deep deterministic policy gradient(AWDDPG)algorithm.And distributed execution and centralized training are adopted to solve the high-dimensional problem.Experiments show that the proposed algorithm can greatly reduce the migration cost and service delay compared with the other related algorithms.

关 键 词：mobile edge computing mobility service migration deep reinforcement learning deep deterministic policy gradient 

分 类 号：TN929.5[电子电信—通信与信息系统] U495[电子电信—信息与通信工程]