Multi-user reinforcement learning based task migration in mobile edge computing
作者机构：Tianjin Key Lab of Intelligent Computing and Novel Software TechnologyTianjin University of TechnologyTianjin 300384China School of Internet of Things EngineeringJiangsu Vocational College of Information TechnologyWuxi 214153China School of Electronic and Information EngineeringBeijing Jiaotong UniversityBeijing 100044China School of Sports Economics and ManagementTianjin University of SportTianjin 301617China
出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)
年 卷 期：2024年第18卷第4期
页 面：161-173页
核心收录：
学科分类：0710[理学-生物学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：mobile edge computing mobility service migration deep reinforcement learning deep deterministic policy gradient
摘 要：Mobile Edge Computing(MEC)is a promising *** service migration is a key technology in *** order to maintain the continuity of services in a dynamic environment,mobile users need to migrate tasks between multiple servers in real *** to the uncertainty of movement,frequent migration will increase delays and costs and non-migration will lead to service ***,it is very challenging to design an optimal migration *** this paper,we investigate the multi-user task migration problem in a dynamic environment and minimizes the average service delay while meeting the migration *** order to optimize the service delay and migration cost,we propose an adaptive weight deep deterministic policy gradient(AWDDPG)*** distributed execution and centralized training are adopted to solve the high-dimensional *** show that the proposed algorithm can greatly reduce the migration cost and service delay compared with the other related algorithms.
维普期刊数据库