Hybrid Bacterial Colony Optimization and Particle Swarm Optimization for Load Balancing in Fog Computing

Kumar, Aryan (2025) Hybrid Bacterial Colony Optimization and Particle Swarm Optimization for Load Balancing in Fog Computing. Masters thesis, Dublin, National College of Ireland.

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Abstract

Fog computing minimizes latency and bandwidth consumption by processing data near the source, but it has the challenge of workload balancing across the dynamic and resource limited fog nodes. Uneven task assignment can result in bottlenecks, idle resources, and lowered Quality of Service (QoS) standards. In this work, we introduce a hybrid metaheuristic load balancing algorithm for fog computing by combining the Bacterial Colony Optimization (BCO) and the Particle Swarm Optimization (PSO) techniques to enhance load balancing. BCO has good solution space exploration capabilities, whereas PSO has the advantage of quick convergence; the hybrid would take advantage from both to achieve reduced make-span with better VM usage. The proposed algorithm has been developed in the Python language, with original BCO and hybrid modules, along with a standard PSO executable. The program has been tested on a synthetic task–VM generation with size ranges from 100 to 10,000 tasks, with identical experiment settings. The results indicate the hybrid BCO–PSO to exhibit reduced make-span with increased VM utilization compared to the individual BCO, PSO, as well as the Adaptive Inertia Weight Particle Swarm Optimization (AIW–PSO) algorithms for the majority of test scenarios, with faster convergence for high workload scenarios. These experimental results indicate that the proposed hybrid algorithm can be an effective, adaptive solution for real-time task allocation in simulated fog-computing scenarios for heterogeneous fog networks.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Gupta, Punit UNSPECIFIED
Subjects:	T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms T Technology > T Technology (General) > Information Technology
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	26 Mar 2026 15:12
Last Modified:	26 Mar 2026 15:12
URI:	https://norma.ncirl.ie/id/eprint/9231

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