Dynamic Task Offloading and Resource Allocation in Vehicle Edge Computing and Networks: A Graph Attention-Based Deep Reinforcement Learning Approach

Vehicle edge computing (VEC) leverages the computational and communication resources available from vehicles and mobile edge computing (MEC) servers to provide computing services for mobile vehicles. However, traditional task offloading and resource allocation methods based on deep reinforcement learning (DRL) often ignore the latent relationships between vehicles and MEC servers. This oversight results in a lack of robustness in highly mobile and complex environments. Therefore, this paper proposes a distributed dynamic task offloading and resource allocation (DDTORA) strategy in the context of vehicle-assisted multi-vehicle VEC scenarios. DDTORA aims to utilize idle computational resources of vehicles and find optimal task offloading and resource allocation schemes to minimize the weighted summation of latency and energy consumption for all tasks. To find the optimal solution, we propose a graph attention network based multi-agent deep reinforcement learning (GAMDRL) algorithm for distributed task offloading and resource allocation. Numerical simulations demonstrate that DDTORA converges faster than the benchmark algorithms, significantly reducing latency and energy consumption by 25.44% to 34.19%.