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

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.