An Adaptive Deep Q-learning Service Migration Decision Framework for Connected Vehicles

The vehicular service support with adaptability, real-time, and low delay is crucial for connected vehicles. However, due to limited coverage of mobile edge computing servers and data processing capability of connected vehicles, vehicular services need to be offloaded to the edge server and adaptively migrate as the connected vehicle moves. Aiming at the adaptive migration service, a deep Q-learning service migration decision algorithm is proposed in this paper. The proposed algorithm can dynamically adjust the vehicular service migration decision according to traffic information. Furthermore, a service migration framework consisting of neural networks is proposed in this paper to improve the adaptability and real-time performance of the algorithm. By using this framework, training and decision-making can be carried out simultaneously in different places. Finally, compared with the two existing algorithms, extensive simulations are conducted to verify the effectiveness of the proposed algorithm.