A Game-Based Computation Offloading Method in Vehicular Multiaccess Edge Computing Networks

Abstract

Multiaccess edge computing (MEC) is a new paradigm to meet the requirements for low latency and high reliability of applications in vehicular networking. More computation-intensive and delay-sensitive applications can be realized through computation offloading of vehicles in vehicular MEC networks. However, the resources of a MEC server are not unlimited. Vehicles need to determine their task offloading strategies in real time under a dynamic-network environment to achieve optimal performance. In this article, we propose a multiuser noncooperative computation offloading game to adjust the offloading probability of each vehicle in vehicular MEC networks and design the payoff function considering the distance between the vehicle and MEC access point, application and communication model, and multivehicle competition for MEC resources. Moreover, we construct a distributed best response algorithm based on the computation offloading game model to maximize the utility of each vehicle and demonstrate that the strategy in this algorithm can converge to a unique and stable equilibrium under certain conditions. Furthermore, we conduct a series of experiments and comparisons with other offloading methods to analyze the effectiveness and performance of the proposed algorithms. The fast convergence and the improved performance of this algorithm are verified by numerical results.