LAUSR

In 5G mobile networks, the convergence of cloud computing and communication leads to mobile edge computing, benefiting vehicular networks. However, the advent of a wide variety of new services and devices has changed the vehicular network landscape, challenging vehicle-to-network services’ migrations. In this paper, we focus on optimizing long-term average latency of multiple services with a different quality of services (QoS). We first introduce an offline algorithm, which can be used to find the optimal migration strategy of services. Then, we analyze the negative effect of trajectory prediction and suggest an optimizing method to reduce this effect by partial updating. Finally, based on this method, we propose a partial dynamic optimization algorithm to approximate the optimal solution, by integrating the priority queue, which utilizes QoS information. We simulate the average service latency, confirming that the proposed partial dynamic optimization algorithm keeps a stable service latency and performs better than other existing algorithms, considering the negative effect of trajectory prediction. We also verify that the proposed algorithm can meet the low latency requirement of the vehicles and the different demands of different services. Besides, the partial dynamic optimization algorithm has a lower time complexity.