Event-Driven Preemptive Priority Scheduling via Causal Topology-Task Context Fusion in Computing Power Networks

Industrial Internet of Things applications like aircraft assembly impose stringent demands on Computing Power Networks. Existing deep reinforcement learning (DRL)-based schedulers not only operate under rigid time-step decision mechanisms but also inadequately handle multi-priority tasks owing to oversimplified queue modeling. To resolve these fundamental limitations, we propose an innovative integrated framework that synergistically combines three components. First, the framework establishes a pioneering formalization of preemptive priority scheduling problem, simultaneously optimizing task response time and violation rate. Second, it incorporates the Counterfactual-Aware Semi-Markov Decision Process (CA-SMDP), which employs counterfactual intervention to tackle temporal credit assignment under event-driven decision epochs. Third, we propose a novel Topology-context fusion Event-driven Scheduler (TESer) where specialized modules for latency minimization and SLA assurance collaboratively achieve optimization synergy. Experimental results demonstrate consistent superiority over state-of-the-art baselines across critical scheduling metrics.