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.
Mindil et al. (Thu,) studied this question.