Distributed Collaborative Computing for Task Completion Rate Maximization in Vehicular Edge Computing
Lei Liu, Z.X. Zhao, Jie Feng, Feng Xu, Yue Zhang, Qingqi Pei, Ming Xiao
Abstract
Benefiting from the outstanding advantages in speeding up task processing and saving energy consumption, vehicular edge computing has entered a period of rapid development. Given the sharp increase in application services, it is vital to fully utilize all available computation resources to guarantee personalized requirements from different users. Specially, a lot of idle vehicle resources can be exploited for task execution to improve the service experience. On the other hand, most works focus on the system performance and fail to guarantee diversified user demands. To this end, we propose a novel distributed collaborative computing scheme for task completion rate maximization (TCRM) in vehicular networks by taking into account both vertical and horizontal collaboration. The novelty of horizontal collaboration lies in the full use of available one-hop vehicle resources for task computing. In order to simultaneously guarantee the system-level performance and the user-level performance, TCRM aims to maximize the task completion rate while minimizing the energy consumption by intelligent resource optimization and task allocation. A TD3-based algorithm combined with the Dirichlet distribution is proposed to obtain the optimization decisions. Extensive simulations demonstrate that TCRM significantly improves performance compared to baseline algorithms.