Game Theory-Based Task Offloading and Resource Allocation for Vehicular Networks in Edge-Cloud Computing
Qinting Jiang, Xiaolong Xu, Qiang He, Xuyun Zhang, Fei Dai, Lianyong Qi, Wanchun Dou
Abstract
With the development of the vehicular network (VN), emerging driver assistance applications are adhibited in daily life. Commonly, edge computing is adopted to satisfy the timeliness requirements of these applications, as the vehicular devices are usually insufficient in computation resources. Nevertheless, the increasing volume of service requests (SRs) are potential to overload the edge servers (ESs), thus increasing the task execution time. Besides, the randomness and the diversity of the SRs also challenge the dynamic resource allocation for the users. To deal with these challenges, a task offloading and resource allocation scheme based on game theory and reinforcement learning (RL) named TORA is proposed. Specifically, game theory is leveraged to determine the optimal task offloading strategy for improving the quality of service (QoS). Meanwhile, RL is applied to implement the dynamic resource allocation of the ES. Finally, the robust performance of the proposed method is validated by comparative experiments.