FIRE: Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations

The author introduces FIRE, a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. The approach involves importance sampling to handle server failures and optimize service migration.