RESCUE: Opportunistic Online Scheduling of Model Retraining on Underutilized Edges

摘要

The proliferation of Artificial Intelligence (AI) applications on edge devices requires frequent edge-assisted model retraining to mitigate data drift. This creates significant resource contention on edge servers, exacerbated by pre-committed high-priority reserved tasks. Instead of investing in costly dedicated servers, we identify a key and overlooked opportunity lying in Underutilized Edge Computing (UEC) resources, which arise from the over-provisioning and fragmentation of these reserved tasks. However, existing scheduling paradigms, designed for dedicated resources, are ineffective at leveraging transient and fragmented UEC environment. To bridge this gap, we introduce RESCUE, a novel online framework that unlocks the UEC’s value for opportunistic retraining. RESCUE enables real-time decision-making under uncertainties in task arrival and retraining duration. It facilitates heterogeneous resource-task assignments and co-schedules reserved tasks to maximize expected profit. We formulate this challenge as a stochastic joint-optimization problem and propose a two-stage approach: the offline stage establishes an optimal benchmark and value functions, while the online stage uses this guidance to make irrevocable scheduling decisions in an uncertain environment. We prove RESCUE achieves a competitive ratio of 1/2. Extensive simulations consistently yield a high empirical competitive ratio between 0.51-0.87, and an average profit increase of over 60% compared to baseline methods. These results indicate RESCUE’s robustness and scalability, establishing its effectiveness for real-world edge systems.