AI-Driven Fairness-Aware Resource Scheduling and Optimization in Constrained Cyber-Physical Infrastructures
DOI:
https://doi.org/10.66280/ijair.v1i1.9Keywords:
cyber-physical systems; resource scheduling; fairness; multi-objective optimization; edge computing; reinforcement learningAbstract
Resource scheduling in constrained cyber-physical infrastructures (e.g., industrial production systems, distribution IoT, and edge collaboration networks) must jointly satisfy latency, energy, reliability, and fairness objectives. Static optimization is often insufficient under rapidly chang- ing workloads and system states. This paper proposes an AI-driven fairness-aware scheduling framework that uses learning-assisted demand prediction and state estimation to guide multi- objective scheduling within feasibility constraints, while enforcing explicit fairness constraints to prevent long-term resource bias under high load. We construct a unified task–resource model with timing and energy constraints, define fairness objectives based on the Jain index and min- imum satisfaction, and introduce a hierarchical solution strategy (prediction–planning–online correction). Across simulated and scenario-based comparisons, the proposed method improves fairness and tail latency while meeting deadline and energy constraints, demonstrating scalabil- ity and practical applicability for multi-constraint CPS infrastructures.
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