Toward Mobility-Aware and Explainable Orchestration in MEC-Enabled Vehicular Networks

Abstract

With the emergence of autonomous vehicles and the ever-increasing volume of generated data, the Mobile Edge Computing paradigm has been proposed to address challenges related to latency and computational capacity. However, static vehicle-to-MEC association policies fail to meet these requirements due to the highly dynamic nature of vehicular networks. To address these challenges, this PhD research focuses on mobilityaware orchestration in MEC-enabled vehicular networks. In our first contribution, we introduce an ETSI-compliant proactive migration framework based on proximity-triggered migration notifications and a mobility-aware task migration strategy to relocate vehicular applications during mobility. The second contribution extends this work toward adaptive orchestration by formulating allocation and migration as a decision-making problem and developing a learning-based framework with a Simu5G–Python interface and a fairness- and delay-aware Maskable PPO agent. Results show improved response time, lower deadline miss rate, and balanced resource utilization under dense vehicular conditions. Our current work focuses on explainability methods to understand the learned policy and support trustworthy deployment while improving performance in terms of E2E delay, deadline miss rate, and fairness in resource utilization.