Game-Based Task Offloading and Resource Allocation for Vehicular Edge Computing With Edge-Edge Cooperation

Abstract

Vehicular Edge Computing (VEC) enables task offloading from vehicles to the edge servers deployed on Road Side Units (RSUs), thus enhancing the task processing performance of the vehicles. However, in a multi-RSU VEC scenario, the uneven geographical distribution of the vehicles naturally causes the load imbalance among the edge servers and leads to the overload and performance degradation problems of the edge servers in hot areas. To this end, in this paper, we propose a joint task offloading and resource allocation for VEC with edge-edge cooperation, in which the tasks offloaded to a high-load edge server can be further offloaded to the other low-load edge servers. Our objective is to minimize the total task processing delay of all the vehicles while guaranteeing the task processing delay tolerance and the holding time of each vehicle. An M/M/1 queue is used to model the task queuing and task computing processes on each RSU. An exact potential game is adopted to model the competition process for the task offloading among the RSUs. A two-stage iterative algorithm is designed to decompose the optimization problem into two stages and solve them iteratively. We analyze the computational complexity of the algorithm and conduct extensive simulations by varying different crucial parameters. The superiority of our scheme is demonstrated in comparison with 3 other reference schemes.