Fine-Tuning and Performance Optimization of a Hybrid Load Balancing Algorithm for Efficient Cloud Computing in a Python-Based Simulation

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 6

Year of Publication : 2025

Authors : F. Niyasudeen, M. Mohan

10.14445/23488549/IJECE-V12I6P116

How to Cite?

F. Niyasudeen, M. Mohan, "Fine-Tuning and Performance Optimization of a Hybrid Load Balancing Algorithm for Efficient Cloud Computing in a Python-Based Simulation," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 6, pp. 206-214, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I6P116

Abstract:

Cloud computing environments require efficient load-balancing mechanisms to prevent bottlenecks, maximize resource utilization, and ensure optimal system performance. While Hybrid Load Balancing Algorithms (HLBA) have superior adaptability compared to existing methods, the effectiveness of these algorithms depends on the fine-tuning of parameters such as load thresholds, balancing approaches, and decision-making models. This study focuses on HLBA optimization by analyzing and adjusting these parameters within the Python-based cloud simulation. The proposed fine-tuning process minimizes response time, increases throughput, and improves server utilization by involving adaptive threshold adjustments, decision-tree optimization, and heuristic-driven enhancements. The results are shown below with different simulated cloud workload environments, assessing the control of parameter tuning on system performance. The comparative analysis with traditional and baseline HLBA implementations confirms that parameter-optimized HLBA achieves a 20–30% development in efficiency. The outcomes provide valuable insights into dynamic parameter tuning for real-time cloud load balancing, paving the way for future AI-driven autonomous load-balancing frameworks.

Keywords:

Adaptive load distribution, Hybrid load balancing, Cloud computing, Optimization, Python simulation.

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