Trajectory prediction-based migration target selection method for vehicular network services

In mobile vehicular networks, when edge servers (ES) provide services to high-speed moving vehicles, the problem of service interruption is particularly prominent due to the limitation of service coverage, which seriously affects the continuity and quality of services. To solve this problem, this paper proposes a service migration target selection method based on trajectory prediction. The method first predicts the future movement trajectories of vehicles by the TS-LSTM trajectory prediction model to identify potential activity areas and their associated edge servers; then, the target server selection is optimized using Deep Q-Network (DQN), which jointly incorporate delay and load fairness into the optimization objective function. In addition, pre-replication technology is introduced during the service migration process to ensure that the original servers can continue to provide services during the service switchover, allowing the target servers to seamlessly receive tasks, effectively ensuring service continuity. The experimental results show that, compared with the current state-of-the-art, the proposed method has significant advantages in terms of convergence speed, service delay and service stability: the average end-to-end service delay is reduced by 32% and the service rejection rate is reduced by 28%.