Improving Load Balancing in Fog Computing using the Polar Fox Optimization Algorithm
Subject Areas : New technologies in distributed systems and algorithmic computingOmid Eslami 1 , Mehdi Effatparvar 2
1 - Department of Computer, Islamic Azad University, Ardabil Branch, Ardabil, Iran
2 - َArdabil Branch, Islamic Azad University, Ardabil, Iran
Keywords: Internet of Things (IoT), Fog Computing, Load Balancing, Meta-heuristic Algorithms, Polar Fox Optimization (PFO),
Abstract :
Fog computing acts as an intermediate layer between edge devices and cloud infrastructures to reduce latency and improve efficiency in time-sensitive Internet of Things (IoT) applications. However, heterogeneous fog nodes and dynamically changing workloads pose major challenges to effective load balancing, often leading to increased response time, higher energy consumption, and inefficient resource utilization. This paper introduces a novel metaheuristic approach, termed the Polar Fox Optimization Algorithm (PFA), for load balancing in fog computing environments. Inspired by the adaptive hunting behavior of polar foxes, the proposed algorithm integrates exploration and exploitation phases to balance global and local search. The PFA algorithm was implemented in Python and MATLAB and evaluated against benchmark methods, including PSO, ACO, GA, GWO, and HHO. Simulation results demonstrate that the proposed approach reduces average latency by up to 18.7%, lowers energy consumption by 14.5%, and improves the load balancing index by 22.3% compared to competing algorithms. Statistical validation using Wilcoxon and Friedman tests (p < 0.05) confirms the superiority of the proposed method. Overall, PFA provides a scalable, stable, and efficient solution for load balancing in dynamic and heterogeneous fog computing environments, particularly for smart cities, real-time healthcare systems, and large-scale IoT applications.
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