Benchmarking Bio-Inspired Metaheuristics for Load-Aware Task Offloading in Hierarchical IoT-Fog-Cloud Ecosystems

Abstract

The multi-objective nature of task scheduling for novel latency-sensitive applications in IoT-Fog-Cloud hierarchies presents persistent challenges, where optimizing one QoS metric often degrades another. Although bio-inspired algorithms provide adaptive solutions, existing comparative studies are constrained by narrow algorithmic scopes, limited evaluation metrics, and a notable absence of statistical validation. To bridge this gap, we introduce a novel unified benchmarking framework that systematically evaluates five prominent metaheuristics — Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Bacterial Foraging Optimization (BFO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC) — for load balancing within a three-tier architecture using the iFogSim simulator. We evaluate performance across three QoS dimensions (response time, makespan, and load imbalance degree) under three escalating workload intensities on heterogeneous infrastructure, with all experiments repeated over 10 independent runs. Statistical significance is assessed via the Wilcoxon signed-rank test against GA, selected as a widely established and computationally stable baseline. Our findings reveal no universally optimal algorithm. GA consistently maintains mean response times below 30.5 ms across all workloads, while PSO and BFO remain statistically indistinguishable from GA on makespan, highlighting their interchangeability under specific conditions. Conversely, ABC uniquely excels in distribution equity, reducing load imbalance from 2.64% to 1.32% as task density increases. ACO, however, incurs statistically significant penalties across all metrics, suffering from a pronounced cloud-bias that elevates load imbalance to 11.0% —up to eight times higher than its counterparts. Collectively, these results confirm the inherent trade-offs between latency, efficiency, and fairness. This reference benchmark delivers a reproducible, statistically validated performance baseline, offering system architects a clear empirical foundation for adaptive algorithm selection in heterogeneous Fog-Cloud environments.