Latency-aware service migration with decision theory for Internet of Vehicles in mobile edge computing.

In the Internet of Vehicles driven by mobile edge computing, the service requests are offloaded to the roadside units (RSUs) via wireless network, reducing the service latency and enhancing the utilization of resources of RSUs. However, the high mobility of vehicles leads to the frequent switching of services, decreasing the quality of service, which fails to meet the requirements of latency-sensitive vehicular services. In this paper, we proposed a latency-aware service migration method with decision theory, named LSMD. Specifically, we first model the network architecture and introduce the transmission and computation of the service requests in detail. Then, considering the high mobility of vehicles, we analyze the dynamic change of vehicle locations and transform the service migration problem into an uncertain decision optimization problem. Afterward, we find the optimal service migration strategy with the objectives of minimizing the service latency and balancing the workload on RSUs. Finally, numerical experiment results on real-world datasets demonstrate that our method outperforms the other two baselines. [ABSTRACT FROM AUTHOR]