Trajectory-aware intelligent service migration in vehicular edge network

Abstract

With the exponent risen of intelligent devices, many applications should process large scale data and provide real-time services with sufficient computing resources and high energy consumption. Traditional cloud computing may bring the great burden to the core network, and cannot meet the requirements of low latency, security, and high quality of service. Thus, edge computing can provide services, with the purpose of reducing latency, saving bandwidth resources, improving efficiency and high quality of service for users. Due to the limitation of the service coverage for each edge servers. When the vehicles move at the road, it may exceed the service range at the edge. Therefore, service migration stratagem should be designed carefully to guarantee the continuity of the service. In sight of the studies of service migration, the mobility and the trajectory are the important factor for service migration, which may reflect the consumption of the service migration. Thus, we propose a trajectory-aware service migration approach with deep reinforcement learning. In this paper, the vehicle trajectory is predicted by the deep spatiotemporal residual network model, and then a service migration algorithm based on Deep reinforcement learning is proposed according to the prediction results. The experimental results show that our algorithm can achieve lower latency comparing with other algorithms.