Active Inference-Enhanced Reinforcement Learning for Adaptive Service Migration in Edge Computing-Enabled Networks

With the widespread adoption of edge computing, service migration is critical for meeting real-time computing demands and ensuring service continuity. However, the dynamic and uncertain nature of edge computing-enabled networks, characterized by fluctuating topologies, bandwidth, and resources, significantly complicates migration decisions. Existing strategies rely on precise analytical models and reward functions but struggle with generalization and adaptability. This paper proposes a novel service migration strategy driven by active inference for edge computing-enabled networks. Unlike traditional approaches, it eliminates the need for explicit reward functions, instead leveraging a cognitive optimization mechanism where decisions are guided by minimizing free energy. This allows the system to maintain efficient service migration across a wider range of edge scenarios, with enhanced generalization and flexibility. Simulation results show that the proposed strategy outperforms existing approaches by reducing latency and improving adaptability to varying environments, highlighting its superiority in service migration for edge computing-enabled networks.