Heterogeneous Task Oriented Data Scheduling in Vehicular Edge Computing via Deep Reinforcement Learning

In vehicular edge computing environment, massive computation-intensive tasks would be produced from diverse vehicular applications. Data scheduling among vehicles and roadside units(RSUs) is a fundamental issue in timely processing those tasks. However, the task heterogeneity with different computation resource requirements and delay constraints, the distinct capacities of vehicles and RSUs, and the stochastic task arrival, pose significant challenges in realizing efficient data scheduling. The existing literature ignores the multi-core feature of both vehicles and RSUs in data scheduling, which may lead to an inefficient resource usage. To cope with these challenges, in this paper, we first construct a multi-queue multi-block model for heterogeneous task oriented data caching on both vehicle and RSU sides. By fully utilizing the multi-core features of both vehicles and RSUs, a fine-grained offloading model is then developed, involving the association between data blocks and computing cores, and the allocation of computation and communication resources. After that, a long-term loss minimization problem is formulated to facilitate data processing. We leverage the Markov decision process (MDP) to model the optimization problem, which is then solved by our proposed deep deterministic policy gradient (DDPG) based association mapping and resource allocation algorithm (D-AMRA). In D-AMRA, an action transformation method is proposed to map the outputs of DDPG to the form of optimization variables. Eventually, extensive simulations with comparative benchmarks are conducted to evaluate the effectiveness of our proposed D-AMRA.