Joint Optimization of Multiuser Computation Offloading and Wireless-Caching Resource Allocation With Linearly Related Requests in Vehicular Edge Computing System

Recently, vehicular edge computing (VEC) has become one of the hottest research fields in Internet of Vehicles (IoV). It provides certain computing, storage, and caching resources at the edge of radio network to execute different kinds of vehicular applications, which can significantly reduce the latency of network operation and service delivery. Also edge caching is an effective way to reduce execution delay and backhaul workload. Undeniably, due to the lack of global information and the time-variety of IoVs, it is a challenge to design a comprehensive execution and resource allocation scheme, including whether to offload and cache, how to offload and cache, and so on. So in this article, we mainly propose a multiuser computation offloading and wireless-caching resource allocation problem with linearly related requests in a VEC system. A multivariable, nonlinear and coupled problem is formulated to minimize the average execution delay, including local execution, interaction among consecutive requests, and mobile edge computing (MEC) execution. Then the deep deterministic policy gradient (DDPG) algorithm is adopted to solve the proposed problem, as it is a strategy learning method for continuous behavior. And simulation results show that our proposed method outperforms other methods in many aspects.