Mobility-Aware Graph Reinforcement Learning for Service Migration in Mobile Edge Computing

Mobile edge computing improves data processing efficiency and reduces latency by deploying computing and storage resources at the network edge, making it suitable for real-time applications. In vehicular networks, due to the high mobility of vehicles and the limited coverage of edge servers, ensuring Quality of Service (QoS) and preventing service inter-ruptions are critical challenges. Service migration and resource reallocation are necessary strategies to maintain QoS. One major challenge in MEC scenarios is how to deliver stable services in the face of high vehicle mobility. To address this, this paper proposes a Mobility-Aware Graph Reinforcement Learning (MA-G RL) framework, designed specifically for service migration in vehicular network systems. The MA-GRL framework consists of two components: the first is a vehicle position prediction module based on a sequence-to-sequence (seq2seq) model, and the second is a graph attention-based reinforcement learning (GRL) module for service migration decision-making and resource allocation. MA-GRL models the vehicular network as a graph and the service migration process as a Markov Decision Process (MDP), utilizing a graph attention network to handle the dynamic observation space and leveraging attention mechanisms to make decisions in a constantly changing action space. Simulation results show that MA-GRL outperforms traditional methods in reducing communication latency. Additionally, the trained model demonstrates adaptability and stability across different network topologies, highlighting its robustness in various environments.