A Reinforcement Learning Based Task Offloading Scheme for Vehicular Edge Computing Network
Recently, the trends of automation and intelligence in vehicular networks have led to the emergence of intelligent connected vehicles (ICVs), and various intelligent applications like autonomous driving have also rapidly developed. Usually, these applications are compute-intensive, and require large amounts of computation resources, which conflicts with resource-limited vehicles. This contradiction becomes a bottleneck in the development of vehicular networks. To address this challenge, the researchers combined mobile edge computing (MEC) with vehicular networks, and proposed vehicular edge computing networks (VECNs). The deploying of MEC servers near the vehicles allows compute-intensive applications to be offloaded to MEC servers for execution, so as to alleviate vehicles’ computational pressure. However, the high dynamic feature which makes traditional optimization algorithms like convex/non-convex optimization less suitable for vehicular networks, often lacks adequate consideration in the existing task offloading schemes. Toward this end, we propose a reinforcement learning based task offloading scheme, i.e., a deep Q learning algorithm, to solve the delay minimization problem in VECNs. Extensive numerical results corroborate the superior performance of our proposed scheme on reducing the processing delay of vehicles’ computation tasks.
KeywordsVehicular edge computing networks Mobile edge computing Reinforcement learning
- 6.Zhang, K., Mao, Y., Leng, S., Vinel, A., Zhang, Y.: Delay constrained offloading for mobile edge computing in cloud-enabled vehicular networks. In: 2016 8th International Workshop on Resilient Networks Design and Modeling (RNDM), pp. 288–294, September 2016. https://doi.org/10.1109/RNDM.2016.7608300
- 7.Liu, Y., Wang, S., Huang, J., Yang, F.: A computation offloading algorithm based on game theory for vehicular edge networks. In: 2018 IEEE International Conference on Communications (ICC), pp. 1–6, May 2018. https://doi.org/10.1109/ICC.2018.8422240
- 13.Ye, H., Li, G.Y.: Deep reinforcement learning for resource allocation in V2V communications. In: 2018 IEEE International Conference on Communications (ICC), pp. 1–6, May 2018. https://doi.org/10.1109/ICC.2018.8422586
- 15.Li, J., Gao, H., Lv, T., Lu, Y.: Deep reinforcement learning based computation offloading and resource allocation for mec. In: 2018 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6, April 2018. https://doi.org/10.1109/WCNC.2018.8377343