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Journal article

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

Abstract

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.

Authors

Li J; Shi Y; Wang X; Zhang Y; Shen W; Zio E

Journal

IEEE Internet of Things Journal, Vol. PP, No. 99, pp. 1–1

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication Date

January 1, 2026

DOI

10.1109/jiot.2026.3653791

ISSN

2327-4662

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