Distributed Game-Theoretical D2D-Enabled Task Offloading in Mobile Edge Computing

作     者：En Wang Han Wang Peng-Min Dong Yuan-Bo Xu Yong-Jian Yang 王恩;王晗;董朋民;徐原博;杨永健

作者机构：Department of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of SoftwareJilin UniversityChangchun 130012China 

出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)

年 卷 期：2022年第37卷第4期

页      面：919-941页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 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] 

基　　金：This work was supported by the National Natural Science Foundation of China under Grant No.62072209 the National Natural Science Foundation of China Youth Fund under Grant No.62002123 the Key Research and Development Program of Jilin Province of China under Grant No.20210201082GX the Scientific and Technological Planning Project of Jilin Province of China under Grant No.JJKH20221010KJ the Development and Reform Commission Project of Jilin Province of China under Grant No.2020C017-2 

主　　题：computation offloading potential game Nash equilibrium device-to-device(D2D) acceptable latency 

摘      要：Mobile Edge Computing(MEC)has been envisioned as a promising distributed computing paradigm where mobile users offload their tasks to edge nodes to decrease the cost of energy and ***,most of the existing studies only consider the congestion of wireless channels as a crucial factor affecting the strategy-making process,while ignoring the impact of offloading among edge *** addition,centralized task offloading strategies result in enormous computation complexity in center *** this line,we take both the congestion of wireless channels and the offloading among multiple edge nodes into consideration to enrich users offloading strategies and propose the Parallel User Selection Algorithm(PUS)and Single User Selection Algorithm(SUS)to substantially accelerate the *** practically,we extend the users offloading strategies to take into account idle devices and cloud services,which considers the potential computing resources at the ***,we construct a potential game in which each user selfishly seeks an optimal strategy to minimize its cost of latency and energy based on acceptable latency,and find the potential function to prove the existence of Nash equilibrium(NE).Additionally,we update PUS to accelerate its convergence and illustrate its performance through the experimental results of three real datasets,and the updated PUS effectively decreases the total cost and reaches Nash equilibrium.