BARGAIN-MATCH: A Game Theoretical Approach for Resource Allocation and Task Offloading in Vehicular Edge Computing Networks

Vehicular edge computing (VEC) is emerging as a promising architecture of vehicular networks (VNs) by deploying the cloud computing resources at the edge of the VNs. However, efficient resource management and task offloading in the VEC network is challenging. In this work, we first present a hierarchical framework that coordinates the heterogeneity among tasks and servers to improve the resource utilization for servers and service satisfaction for vehicles. Moreover, we formulate a joint resource allocation and task offloading problem (JRATOP), aiming to jointly optimize the intra-VEC server resource allocation and inter-VEC server load-balanced offloading by stimulating the horizontal and vertical collaboration among vehicles, VEC servers, and cloud server. Since the formulated JRATOP is NP-hard, we propose a cooperative resource allocation and task offloading algorithm named BARGAIN-MATCH, which consists of a bargaining-based incentive approach for intra-server resource allocation and a matching method-based horizontal-vertical collaboration approach for inter-server task offloading. Besides, BARGAIN-MATCH is proved to be stable, weak Pareto optimal, and polynomial complex. Simulation results demonstrate that the proposed approach achieves superior system utility and efficiency compared to the other methods, especially when the system workload is heavy.

I. Introduction

With the development of vehicular networks (VNs) and the ever-increasing number of vehicles on the road, various and explosive applications are emerging such as autonomous driving, auto navigation, and augmented reality. These vehicular applications usually require extensive computation resources and low or ultra-low latency. However, fulfilling the computation-intensive and delay-sensitive tasks is challenging due to the limited computation resources of vehicles. To overcome this challenge, mobile edge computing or multi-access edge computing (MEC) [1] is emerging as a promising technology by shifting the cloud computing resources in close proximity to mobile terminals, leading to the new paradigm of vehicular edge computing (VEC) [1], [2], [3]. The VEC migrates the lightweight and ubiquitous resources from cloud servers to the road side units (RSUs) equipped with VEC servers to extend the computation capabilities of the conventional VNs [4]. By offloading the tasks to the VEC servers, the communication latency between the vehicles and the cloud server can be reduced, and the computation overloads on vehicles can be relieved.