Sungwon Moon, Yujin Lim

KIPS Transactions on Computer and Communication Systems, Vol. 10, No. 12, pp. 311-318, Dec. 2021
https://doi.org/10.3745/KTSDE.2021.10.12.311,   PDF Download:   Full Text
Keywords: Task Migration, Vehicular Edge Computing, Reinforcement Learning, DQN
Abstract

With the rapid development of the Internet of Things(IoT) technology recently, multi-access edge computing(MEC) is emerged as a next-generation technology for real-time and high-performance services. High mobility of users between MECs with limited service areas is considered one of the issues in the MEC environment. In this paper, we consider a vehicle edge computing(VEC) environment which has a high mobility, and propose a task migration algorithm to decide whether or not to migrate and where to migrate using DQN, as a reinforcement learning method. The objective of the proposed algorithm is to improve the system throughput while satisfying QoS(Quality of Service) requirements by minimizing the difference between queueing delays in vehicle edge computing servers(VECSs). The results show that compared to other algorithms, the proposed algorithm achieves approximately 14-49% better QoS satisfaction and approximately 14–38% lower service blocking rate.

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