Efficient Multi-User for Task Offloading and Server Allocation in Mobile Edge Computing Systems

作     者：Qiuming Liu Jing Li Jianming Wei Ruoxuan Zhou Zheng Chai Shumin Liu Qiuming Liu;Jing Li;Jianming Wei;Ruoxuan Zhou;Zheng Chai;Shumin Liu

作者机构：School of Software EngineeringJiangxi University of Science and TechnologyNanchang 330013China Nanchang Key laboratory of Virtual Digital Factory and Cultural CommunicationsNanchang 330013China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2022年第19卷第7期

页      面：226-238页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 12[管理学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0810[工学-信息与通信工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 0835[工学-软件工程] 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：presented in part at the EAI CHINACOM 2020 supported in part by Natural Science Foundation of Jiangxi Province (Grant No.20202BAB212003) Projects of Humanities and Social Sciences of universities in Jiangxi (JC18224) Science and technology project of Jiangxi Provincial Department of Education(GJJ210817, GJJ210854) 

主　　题：distributed unsupervised learning energy efficiency mobile edge computing task offloading 

摘      要：Mobile edge computing has emerged as a new paradigm to enhance computing capabilities by offloading complicated tasks to nearby cloud *** conserve energy as well as maintain quality of service,low time complexity algorithm is proposed to complete task offloading and server *** this paper,a multi-user with multiple tasks and single server scenario is considered for small network,taking full account of factors including data size,bandwidth,channel state ***,we consider a multi-server scenario for bigger network,where the influence of task priority is taken into *** jointly minimize delay and energy cost,we propose a distributed unsupervised learning-based offloading framework for task offloading and server *** exploit a memory pool to store input data and corresponding decisions as key-value pairs for model to learn to solve optimization *** further reduce time cost and achieve near-optimal performance,we use convolutional neural networks to process mass data based on fully connected *** results show that the proposed algorithm performs better than other offloading schemes,which can generate near-optimal offloading decision timely.