Mobility-Aware Cooperative Task Offloading and Resource Allocation in Vehicular Edge Computing
Yifan Zhang, Xiaoqi Qin, Xianxin Song
Abstract
Vehicular edge computing (VEC) is considered as a promising technology to support emerging applications in vehicular networks by leveraging the ubiquitous computing resource at network edge. To leverage the excessive computing power on road side units (RSUs) and neighboring vehicles, effective collaboration among the vehicles and between vehicles and RSUs is required. The high mobility of vehicles and the ad hoc nature of networking introduce difficulties in effective task scheduling and reliable result feedback. In this paper, we study a joint task offloading and resource allocation problem in vehicular networks with the aim of optimizing the system utility related to delay and cost of computing and communication services. The problem formulation involves joint consideration of mobility of vehicles, task offloading decision, computing resource allocation, and reliable result feedback. The formulated problem falls in the form of a mixed- integer nonlinear program (MINLP). To solve the problem efficiently, we propose a matching based task offloading and resource allocation algorithm. Through simulation results, we show that the performance of our proposed scheme is competitive when compared with existing strategies.