Open Access

ARTICLE

Evolutionary Algorithm Based Adaptive Load Balancing (EA-ALB) in Cloud Computing Framework

J. Noorul Ameen^1,*, S. Jabeen Begum²

1 Department of Computer Science and Engineering, E.G.S Pillay Engineering College, Nagapattinam, 611002, India
2 Department of Computer Science and Engineering, Velalar College of Engineering and Technology, Erode, 638012, India

* Corresponding Author: J. Noorul Ameen. Email:

Intelligent Automation & Soft Computing 2022, 34(2), 1281-1294. https://doi.org/10.32604/iasc.2022.025137

Received 13 November 2021; Accepted 25 January 2022; Issue published 03 May 2022

Abstract

In the present decade, the development of cloud computing framework is witnessed for providing computational resources by dynamic service providing methods. There are many problems in load balancing in cloud, when there is a huge demand for resources. The objective of load balancing is to equilibrate the cloud server computations for avoiding overloading problems. On addressing the issue, this paper develops a new model called Evolutionary Algorithm based Adaptive Load Balancing (EA-ALB) for enhancing the efficacy and user satisfaction of cloud services. Efficient Scheduling Scheme for the virtual machines using machine learning algorithm is proposed in this work. Initially, process of K-means clustering is used for computing optimal min-max rates and then, local search capability for solving the load balancing problems in cloud model is determined with the incorporation of Evolutionary Algorithm. The results show that the proposed model achieves better results in terms of load balancing factors, Virtual Machine (VM) migration, energy consumption and so on, when compared to the existing model.

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Keywords

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