Deep Reinforcement Learning-Based Collaborative Computation Offloading for Distributed Vehicular Edge Computing

In Vehicular Edge Computing (VEC), apart from the Road Side Units (RSUs) that can undertake the computation, smart vehicles that incorporate high-end multi-core processors into On-Board Units (OBU) can also contribute their computing resources for vehicular tasks in a pay-as-you-go fashion. Designing an appropriate pricing strategy for vehicles with abundant computing resources is essential yet challenging, as it requires balancing profit-seeking objectives with the needs of service requestors. On the other hand, considering the perspective of vehicles with offloading requests, task offloading should strike a balance between achieving ultra-low task latency and minimizing the associated offloading costs. To tackle these issues, we propose a Collaborative Computation Offloading Scheme (CCOS) for the VEC system. In particular, we take into account the fluctuation of service pricing, to cater to the monetary constraints of service requesters. A Mixed-Integer Nonlinear Programming (MINLP) problem is formulated to minimize the weighted sum of task completion latency and the offloading costs. The optimization problem is decomposed into two subproblems, i.e., the task offloading problem and the computing resource allocation problem, respectively. The task offloading problem is essentially a combinatorial optimization problem that necessitates exponential time complexity for determining the optimal solution. Hence, a Deep Reinforcement Learning (DRL)-based algorithm is put forward to solve this subproblem. The resource allocation problem, however, has been proven to be a convex optimization problem, and the scheduling and allocation of computing resources can be performed in parallel, since each edge node is aware of its own task offloading requests. Simulation results demonstrate that our strategy outperforms other approaches in terms of the convergence rate, task completion rate, and optimal values.