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| Title | An Online Reinforcement Learning Offloading Method For Delay-Sensitive Vehicular Service |
|---|---|
| ID_Doc | 8798 |
| Authors | Liu W.; Shao X.; Wang C.; Gu X.; Jiang F.; Peng J. |
| Year | 2020 |
| Published | Proceedings - 2020 IEEE 22nd International Conference on High Performance Computing and Communications, IEEE 18th International Conference on Smart City and IEEE 6th International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020 |
| DOI | http://dx.doi.org/10.1109/HPCC-SmartCity-DSS50907.2020.00130 |
| Abstract | Nowadays, various advanced vehicular applications are developed for road safety enhancement and traffic optimization, while the limited computation capability of vehicles can not always meet the delay requirements of these applications. Offloading the computation tasks to edge servers is regarded as a promising solution. However, how to select the optimal edge server to offload so as to support many delay-sensitive vehicular services is a challenging problem, especially when the server status changes. To overcome this challenge, this paper proposes an online reinforcement learning offloading method. The offloading decision process is divided into two steps to reduce the computation cost. Firstly, the edge server candidates are obtained through analysis on the historical server data, using the reinforcement learning method. Secondly, the optimal edge server is selected from the candidates online through observing current condition of the status of servers. Simulation results verify the superiority of the proposed method and it can effectively reduce running time. © 2020 IEEE. |
| Author Keywords | computation offloading; edge computing; reinforcement learning; vehicular network |
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