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(Department of Computer Science and Technology,Jilin University,Changchun 130012,China;Department of Software,Jilin University,Changchun 130012,China)

机构地区：[1]Department of Computer Science and Technology,Jilin University,Changchun 130012,China [2]Department of Software,Jilin University,Changchun 130012,China

出　　处：《Journal of Computer Science & Technology》2022年第4期919-941,共23页计算机科学技术学报（英文版）

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.

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 computation.However,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 nodes.In addition,centralized task offloading strategies result in enormous computation complexity in center nodes.Along 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 convergence.More practically,we extend the users'offloading strategies to take into account idle devices and cloud services,which considers the potential computing resources at the edge.Furthermore,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.

关 键 词：computation offloading potential game Nash equilibrium device-to-device(D2D) acceptable latency 

分 类 号：TN929.5[电子电信—通信与信息系统]