Recent advances in networking, caching and computing have significant impacts on the developments of vehicular networks. Nevertheless, these important enabling technologies have traditionally been studied separately in the existing works on vehicular networks. In this paper, we propose an integrated framework that can enable dynamic orchestration of networking, caching and computing resources to improve the performance of next generation vehicular networks.We formulate the resource allocation strategy in this framework as a joint optimization problem. The complexity of the system is very high when we jointly consider these three technologies. Therefore, we propose a novel deep reinforcement learning approach in this paper. Simulation results are presented to show the effectiveness of the proposed scheme.

Additional Metadata
Keywords Caching, Deep reinforcement learning, Edge computing, Vehicular networks
Persistent URL dx.doi.org/10.1109/VTCFall.2017.8288203
Conference 86th IEEE Vehicular Technology Conference, VTC Fall 2017
Citation
He, Y. (Ying), Liang, C. (Chengchao), Zhang, Z. (Zheng), Yu, F.R, Zhao, N. (Nan), Yin, H. (Hongxi), & Zhang, Y. (Yanhua). (2018). Resource allocation in software-defined and information-centric vehicular networks with mobile edge computing. In IEEE Vehicular Technology Conference (pp. 1–5). doi:10.1109/VTCFall.2017.8288203