Research on Dependency-Aware Service Migration Strategy in the Internet of Vehicles Integrating a Graph Attention Network and Deep Reinforcement Learning

The integration of Mobile Edge Computing and container virtualization technologies provides crucial support for low-latency and highly resilient service deployment in Internet of Vehicles (IoV) applications. However, the high mobility of vehicles poses challenges to service continuity, necessitating dynamic adjustment of service deployment locations through container migration. Existing research predominantly focuses on independent service migration while overlooking the complex interdependencies among multiple subtasks in practical applications. In this paper, we investigate the container migration problem for dependency-aware services in IoV environments. We first formulate the problem as a dual-objective optimization problem centered on minimizing both the average service delay and system load imbalance. To address the complex dependencies among containers and the highly dynamic nature of IoV environments, we propose an intelligent migration algorithm named GADM that integrates Graph Attention Networks with Deep Reinforcement Learning. The GADM algorithm leverages Graph Attention Networks to capture critical paths in task dependencies, and combines this with an actor–critic-based Deep Reinforcement Learning framework to achieve adaptive decision-making in dynamic environments. Validation using real-world vehicle trajectory datasets and Alibaba cluster trace datasets demonstrates the effectiveness of the proposed algorithm. Experimental results indicate that compared to other methods, GADM significantly improves system load balancing while reducing average service latency.