Multi-Service Dependent Task Offloading Based on Multi-Agent DRL for Vehicular Edge Computing

Vehicle edge computing provides abundant computing and storage resources at the network edge, aiming to meet the growing demand of latency-sensitive vehicular applications. Efficiently utilizing these resources is crucial for improving task execution efficiency in vehicular networks. However, existing research often overlooks the limited service capacity of edge servers and the dependencies present in vehicular applications, resulting in suboptimal offloading decisions. Additionally, the high mobility of vehicles introduces further challenges in making optimal offloading decisions. To address these issues, this paper proposes a task offloading algorithm based on Multi-Agent Deep Reinforcement Learning (MADRL) for vehicular applications with dependencies and diverse service requirements. The algorithm comprehensively considers vehicle mobility and the limited service capacity of edge servers to meet heterogeneous Quality of Service (QoS) demands in a heterogeneous vehicular network. In the proposed algorithm, we first decompose dependent task offloading into a sequential offloading process and model this process as a Markov Decision Process (MDP). The Multi-Agent Deep Deterministic Policy Gradient(MADDPG) algorithm is then applied to solve the problem. Simulation results show that the proposed scheme has better performance in reducing average task processing latency.