Deep Reinforcement Learning Empowered Edge Collaborative Caching Scheme for Internet of Vehicles

Abstract

With the development of internet of vehicles, the traditional centralized content caching mode transmits content through the core network, which causes a large delay and cannot meet the demands for delay-sensitive services. To solve these problems, on basis of vehicle caching network, we propose an edge collaborative caching scheme. Road side unit (RSU) and mobile edge computing (MEC) are used to collect vehicle information, predict and cache popular content, thereby provide low-latency content delivery services. However, the storage capacity of a single RSU severely limits the edge caching performance and cannot handle intensive content requests at the same time. Through content sharing, collaborative caching can relieve the storage burden on caching servers. Therefore, we integrate RSU and collaborative caching to build a MEC-assisted vehicle edge collaborative caching (MVECC) scheme, so as to realize the collaborative caching among cloud, edge and vehicle. MVECC uses deep reinforcement learning to predict what needs to be cached on RSU, which enables RSUs to cache more popular content. In addition, MVECC also introduces a mobility-aware caching replacement scheme at the edge network to reduce redundant cache and improving cache efficiency, which allows RSU to dynamically replace the cached content in response to the mobility of vehicles. The simulation results show that the proposed MVECC scheme can improve cache performance in terms of energy cost and content hit rate.