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

arXiv:2209.14399v2 Announce Type: replace-cross
Abstract: In edge computing, users' service profiles are migrated due to user mobility. Reinforcement learning (RL) frameworks have been proposed to do so, often trained on simulated data. However, existing RL frameworks overlook occasional server failures, which although rare, impact latency-sensitive applications like autonomous driving and real-time obstacle detection. Nevertheless, these failures (rare events), being not adequately represented in historical training data, pose a challenge for data-driven RL algorithms. As it is impractical to adjust failure …

abstract applications arxiv autonomous autonomous driving computing cs.lg cs.ni cs.sy data driving edge edge computing eess.sy failure fire framework frameworks however impact latency migrations mobility profiles reinforcement reinforcement learning server service simulated data type