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<p>The rapid expansion of real-time, latency-sensitive Internet-of-Things (IoT) applications has revealed the limits of centralized cloud infrastructures and driven computation toward a hierarchical IoT–fog–cloud continuum. Scheduling in this environment is challenging due to resource heterogeneity, dynamic arrivals and deadlines, and competing objectives such as latency, energy, and cost. This paper introduces Quantum-inspired Biased Dynamic Scheduler (QBDS), a novel scheduling framework that optimizes a configurable Composite Objective Function (COF) combining makespan, total energy consumption, total cost, load balance, resource utilization, and temporal metrics (waiting and response times). QBDS uses (1) a priority-aware task ranking that adaptively weights deadline slack, execution length, memory footprint and data size to form a globally informed execution order; (2) a sinusoidal, quantum-inspired biasing mechanism that perturbs normalized task and node metrics via randomized mixing weights and sine modulation to escape local optima and encourage exploration of underused resources; and (3) a penalty-aware multi-objective cost evaluator for assignment decisions. Extensive experiments, including ablation studies and comparisons with state-of-the-art metaheuristics and classical heuristics, demonstrate that QBDS consistently improves makespan, energy, cost, and resource utilization across diverse workloads and topologies, while scaling robustly under heavy load.</p>

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A novel approach for dynamic task scheduling for IOT in fog-cloud environment

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月22日

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  • A. Mindil,
  • Ahmed Y. Hamed,
  • Moatamad R. Hassan,
  • M. Kh. Elnahary

摘要

The rapid expansion of real-time, latency-sensitive Internet-of-Things (IoT) applications has revealed the limits of centralized cloud infrastructures and driven computation toward a hierarchical IoT–fog–cloud continuum. Scheduling in this environment is challenging due to resource heterogeneity, dynamic arrivals and deadlines, and competing objectives such as latency, energy, and cost. This paper introduces Quantum-inspired Biased Dynamic Scheduler (QBDS), a novel scheduling framework that optimizes a configurable Composite Objective Function (COF) combining makespan, total energy consumption, total cost, load balance, resource utilization, and temporal metrics (waiting and response times). QBDS uses (1) a priority-aware task ranking that adaptively weights deadline slack, execution length, memory footprint and data size to form a globally informed execution order; (2) a sinusoidal, quantum-inspired biasing mechanism that perturbs normalized task and node metrics via randomized mixing weights and sine modulation to escape local optima and encourage exploration of underused resources; and (3) a penalty-aware multi-objective cost evaluator for assignment decisions. Extensive experiments, including ablation studies and comparisons with state-of-the-art metaheuristics and classical heuristics, demonstrate that QBDS consistently improves makespan, energy, cost, and resource utilization across diverse workloads and topologies, while scaling robustly under heavy load.