Joint Task Offloading and Resource Allocation for Collaborative VEC Network

Vehicular edge computing (VEC) is a promising technique for handling computation-intensive and delay-sensitive tasks by offloading them to roadside units (RSUs) or base stations (BSs) equipped with edge computing servers. However, the uneven spatial-temporal distribution of vehicles causes load imbalances among edge servers. To address this challenge, we propose a two-layer collaborative VEC network paradigm that incorporates offloading modes such as vehicle-to-RSU (V2R), vehicle-to-BS (V2B), and RSU-to-RSU collaboration. Within this framework, task offloading and resource allocation are jointly optimized to maximize system utility, which integrates revenue, delay, and energy consumption, while ensuring that vehicular tasks meet their delay requirements. Given the problem’s complexity and scalability concerns, we introduce a distributed framework and propose the joint task offloading and resource allocation (JTORA) algorithm. This algorithm decomposes the original problem into two sub-problems: task offloading and resource allocation. The task offloading sub-problem is modeled as a potential game and solved using the multi-agent twin delayed deep deterministic policy gradient (MATD3) framework. Based on the offloading decisions, the resource allocation sub-problem is further divided into multiple convex optimization problems. The system utility, derived from task offloading and resource allocation decisions, serves as a reward to iteratively evaluate, train the learning model, and refine the offloading strategy. Theoretical analysis confirms that the JTORA algorithm converges to the Nash equilibrium (NE). Simulations using real traffic data validate the proposed algorithm’s effectiveness and superiority over existing methods. Specifically, the JTORA algorithm improves overall system utility by reducing task processing delays and energy consumption while increasing the task completion rate.