@article{M8005F501,
title = "Task Migration in Cooperative Vehicular Edge Computing",
journal = "KIPS Transactions on Computer and Communication Systems",
year = "2021",
issn = "2287-5891",
doi = "https://doi.org/10.3745/KTSDE.2021.10.12.311",
author = "Sungwon Moon/Yujin Lim",
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."
}