Sungwon Moon  Yujin Lim

10.5626/JOK.2021.48.2.243

Q-learning Vehicular Edge Computing Service migration Reinforcement Learning

As edge computing can provide low latency and real-time services, it is emerging as a promising technology that can lead the Internet of things(IoT). However, user mobility and limited coverage of edge computing result in service interruption and reduce Quality of Service(QoS). Thus, service migration is considered an important issue to guarantee seamless service. In this paper, a migration decision algorithm is proposed using Q-learning, as a reinforcement learning method in the vehicular edge computing environment. The proposed algorithm decides whether or not to migrate and where to migrate in order to meet delay constraint and minimize system cost. In the performance evaluation, we compared propsed algorithm with other algorithms in terms of deciding whether or not to migrate and where to migrate, and our proposed algorithm shows better performance compared to the other algorithms.