Machine Learning for Load Balancing in Cloud Computing: A Comprehensive Study with Experimental Data and Analysis

Authors: Sheetanshu Rajoriya, Laxman Singh Gour

Country: India

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Abstract: Load balancing plays a crucial role in the efficient functioning of cloud computing infrastructure, as it helps to make the most of available resources and enhance performance levels. However, traditional load balancing methods face challenges when it comes to adapting to the ever-changing and intricate nature of cloud environments. To address this issue, this research paper delves into a comprehensive exploration of machine learning techniques specifically tailored for load balancing in cloud computing. The primary focus lies on assessing the efficacy of these techniques in managing dynamic workloads and enhancing resource allocation. To achieve this, we conduct a series of experiments using real-world data, enabling us to thoroughly evaluate the performance of various machine learning models. By doing so, we aim to provide an extensive analysis that sheds light on the strengths and limitations of these models, thereby offering valuable insights to the field.

Keywords: Machine Learning, Load Balancing, Cloud Computing, Resource Utilization, Response Time, Dynamic Workload, Neural Networks, Support Vector Machines, Random Forest, K-means Clustering

Paper Id: 1709

Published On: 2013-12-11

Published In: Volume 1, Issue 2, November-December 2013

Cite This: Machine Learning for Load Balancing in Cloud Computing: A Comprehensive Study with Experimental Data and Analysis - Sheetanshu Rajoriya, Laxman Singh Gour - IJIRMPS Volume 1, Issue 2, November-December 2013.

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