Open Access

ARTICLE

ACO-Inspired Load Balancing Strategy for Cloud-Based Data Centre with Predictive Machine Learning Approach

by Niladri Dey¹, T. Gunasekhar¹, K. Purnachand^2,*

1 Department of CSE, K. L. University, Vaddeswaraam, 522302, Andhra Pradesh, India
2 Department of CSE (Data Science), B. V. Raju Institute of Technology, TS, 502313, India

* Corresponding Author: K. Purnachand. Email:

Computers, Materials & Continua 2023, 75(1), 513-529. https://doi.org/10.32604/cmc.2023.035139

Received 09 August 2022; Accepted 15 November 2022; Issue published 06 February 2023

Abstract

Virtual Machines are the core of cloud computing and are utilized to get the benefits of cloud computing. Other essential features include portability, recovery after failure, and, most importantly, creating the core mechanism for load balancing. Several study results have been reported in enhancing load-balancing systems employing stochastic or biogenetic optimization methods. It examines the underlying issues with load balancing and the limitations of present load balance genetic optimization approaches. They are criticized for using higher-order probability distributions, more complicated solution search spaces, and adding factors to improve decision-making skills. Thus, this paper explores the possibility of summarizing load characteristics. Second, this study offers an improved prediction technique for pheromone level prediction over other typical genetic optimization methods during load balancing. It also uses web-based third-party cloud service providers to test and validate the principles provided in this study. It also reduces VM migrations, time complexity, and service level agreements compared to other parallel standard approaches.

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Keywords

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