Joint Task Migration and Resource Allocation in Vehicular Edge Computing: A Deep Reinforcement Learning-Based Approach

Abstract

With the rise of the Internet of Vehicles (IoV), a growing number of in-vehicle applications have been developed, significantly enhancing the driving experience, while simultaneously imposing excessive higher demands on computing resources. Vehicle Edge Computing (VEC) emerges as a promising solution by offloading computational tasks to edge servers positioned near vehicles. However, the limited computing capacity of these edge servers necessitates the efficient allocation of resources to adequately meet the demands of all vehicles. In this paper, we introduce a multi-vehicle VEC offloading framework that considers both the execution time of tasks and the costs incurred when utilizing edge server resources and transmitting tasks between Roadside Units (RSUs). Building on this framework, we design an optimization problem to minimize the average weighted cost, modeled as a Markov Decision Process (MDP). To address this, we propose a DDPG-based Resource Allocation and Offloading Decision Algorithm (DRAODA). This algorithm enables the control center to generate resource allocation strategies while allowing individual vehicles to make task offloading decisions independently, based solely on their own data and the edge server's status, without relying on information from other vehicles. Additionally, we propose an Optimal Task Offloading Destination Selection Algorithm (OTODSA) to further minimize the average weighted cost, enhancing the overall efficiency and effectiveness of the resource allocation process.