Road Side Unit-Assisted Learning-Based Partial Task Offloading for Vehicular Edge Computing System
Song Li, Weibin Sun, Qiang Ni, Yanjing Sun
Abstract
The rapid development of vehicular networks creates diverse ultra-low latency constrained and computation-intensive applications, which bring challenges to both communication and computation capabilities of the vehicles and their transmission. By offloading tasks to the edge servers or vehicles in the neighbourhood, vehicular edge computing (VEC) provides a cost-efficient solution to this problem. However, the channel state information and network structure in the vehicular network varies fast because of the inherent mobility of vehicle nodes, which brings an extra challenge to task offloading. To address this challenge, we formulate the task offloading in vehicular network as a multi-armed bandit (MAB) problem and propose a novel road side unit (RSU)-assisted learning-based partial task offloading (RALPTO) algorithm. The algorithm enables vehicle nodes to learn the delay performance of the service provider while offloading tasks. Specifically, the RSU could assist the learning process by sharing the learning information among vehicle nodes, which improves the adaptability of the algorithm to the time-varying networks. Simulation results demonstrate that the proposed algorithm achieves lower delay and better learning performance compared with the benchmark algorithms.