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VOL. 1, ISSUE 1 (2025)
A comprehensive study on multi-objective bio-inspired load balancing techniques for efficient cloud computing environments
Authors
Brototi Mondal
Abstract
The rapid expansion of cloud computing has
intensified the demand for efficient load balancing mechanisms capable of
optimizing multiple conflicting objectives, such as response time, energy
consumption, and resource utilization. This study presents a comprehensive
analysis of multi-objective bio-inspired techniques for load balancing in cloud
environments. The primary objective is to evaluate how nature-inspired
optimization algorithms—such as Genetic Algorithms (GA), Particle Swarm
Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony
(ABC)—can be effectively adapted to balance workloads across distributed cloud
resources. The research employs a comparative experimental framework using
simulated cloud infrastructures on CloudSim to assess algorithmic performance
under varying workload intensities and resource heterogeneity. Results indicate
that multi-objective bio-inspired approaches outperform traditional heuristic
and rule-based methods by achieving up to 23% improvement in task completion
time, 18% reduction in energy consumption, and 15% enhancement in overall
resource utilization. Among the examined algorithms, the hybrid PSO-GA model
demonstrated the most balanced trade-off between performance and stability
across diverse scenarios. The study also highlights the scalability and
adaptability of bio-inspired methods, making them particularly suitable for
dynamic and large-scale cloud systems. The findings suggest that
integrating multi-objective optimization with bio-inspired principles provides
a promising pathway for enhancing the sustainability and efficiency of cloud
computing infrastructures. Future work may explore real-time adaptive models
and hybridization strategies to further improve decision-making under uncertain
and rapidly changing workloads.
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Pages:20-25
How to cite this article:
Brototi Mondal "A comprehensive study on multi-objective bio-inspired load balancing techniques for efficient cloud computing environments". World Journal of Physics and Applications, Vol 1, Issue 1, 2025, Pages 20-25
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