A Two-stage Service-oriented Task Offloading Framework with Edge-cloud Collaboration:A Game Theory Approach  

作　　者：Shiyong Li Wenzhe Li Huan Liu Wei Sun 

机构地区：[1]School of Economics and Management,Yanshan University,Qinhuangdao 066004

出　　处：《Journal of Systems Science and Systems Engineering》2024年第5期521-551,共31页系统科学与系统工程学报（英文版）

基　　金：the National Natural Science Foundation of China under Grant No.71971188;the Humanity and Social Science Foundation of Ministry of Education of China under Grant No.22YJCZH086;the Hebei Natural Science Foundation under Grant Nos.G2022203003 and G2023203008;the support Funded by Science Research Project of Hebei Education Department under Grant No.ZD2022142.

摘　　要：With the fast development of Mobile Internet,data traffic generated by end devices is anticipated to witness substantial growth in the future years.However,processing tasks locally will cause latency due to the limited resources of the end devices.Edge-cloud collaboration,an effective solution for latency-sensitive applications,is attracting greater attention from both industry and academia.It combines the advantages of the cloud center with abundant computing resources and edge nodes with low-latency capabilities.In this paper,we propose a two-stage task offloading framework with edge-cloud collaboration to assist end devices processing latency-sensitive tasks either on the edge servers or in the cloud center.As for homogeneous task offloading,in the first stage,the competitive end devices offload tasks to the edge gateways.We formulate the selfish task offloading problem among end devices as a potential game.In the second stage,the edge nodes request resources from the cloud center to process end devices tasks due to their limited resources.Then,we consider the heterogeneous task offloading problem and use intelligent optimization algorithm to obtain the optimal offloading strategy.Simulation results show that the service prices of edge nodes influence the decisions and task offloading costs of end devices.We also verify the intelligent optimization algorithm can achieve optimal performance with low complexity and fast convergence.

关 键 词：Edge-cloud collaboration task offloading potential game simulation annealing algorithm 

分 类 号：TN929.5[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程]