<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Cloud remains an active and dominant player in the field of information technology. Hence, to meet the rapidly growing requirement of computational processes and storage resources, the cloud provider deploys efficient data centres globally that comprise thousands of IT servers. Because of tremendous energy and resource utilization, a reliable cloud platform has to be necessarily optimized. Effective load balancing is a great option to overcome these issues. However, loading balancing difficulties, such as increased computational complexity, the chance of losing the client data during task rescheduling, and consuming huge memory of the host, and new VM (Virtual Machine), need appropriate optimization. Hence, the study aims to create a newly developed IG-WA (Inquisitive Genetic–Wolf Optimization) framework that meritoriously detects the optimized virtual machine in an environment. For this purpose, the system utilises the GWO (Grey Wolf Optimization) method with an evolutionary mechanism for achieving a proper compromise between exploitation and exploration, thereby accelerating the convergence and achieving optimized accuracy. Furthermore, the fitness function evaluated with an inquisitive genetic algorithm adds value to the overall efficacy. Performance evaluation brings forward the outperformance of the proposed IGWO system in terms of energy consumption, execution time and cost, makespan, CPU utilization, and memory utilization. Further, the system attains more comprehensive and better results when compared to the state of art methods.</p>
</abstract>

Cloud remains an active and dominant player in the field of information technology. Hence, to meet the rapidly growing requirement of computational processes and storage resources, the cloud provider deploys efficient data centres globally that comprise thousands of IT servers. Because of tremendous energy and resource utilization, a reliable cloud platform has to be necessarily optimized. Effective load balancing is a great option to overcome these issues. However, loading balancing difficulties, such as increased computational complexity, the chance of losing the client data during task rescheduling, and consuming huge memory of the host, and new VM (Virtual Machine), need appropriate optimization. Hence, the study aims to create a newly developed IG-WA (Inquisitive Genetic–Wolf Optimization) framework that meritoriously detects the optimized virtual machine in an environment. For this purpose, the system utilises the GWO (Grey Wolf Optimization) method with an evolutionary mechanism for achieving a proper compromise between exploitation and exploration, thereby accelerating the convergence and achieving optimized accuracy. Furthermore, the fitness function evaluated with an inquisitive genetic algorithm adds value to the overall efficacy. Performance evaluation brings forward the outperformance of the proposed IGWO system in terms of energy consumption, execution time and cost, makespan, CPU utilization, and memory utilization. Further, the system attains more comprehensive and better results when compared to the state of art methods.

Inquisitive Genetic-Based Wolf Optimization for Load Balancing in Cloud Computing

Department of Computer Science and Engineering

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 International License.

Cloud remains an active and dominant player in the field of information technology. Hence, to meet the rapidly growing requirement of computational processes...

Inquisitive Genetic-Based Wolf Optimization for Load Balancing in Cloud Computing

Online veröffentlicht: 17. Aug. 2023

Seitenbereich: 170 - 179

DOI: https://doi.org/10.2478/acss-2023-0017

Schlüsselwörter
Grey wolf optimization, inquisitive genetic algorithm, load balancing, task allocation, virtual machine

© 2023 Suman Sansanwal et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Inquisitive Genetic-Based Wolf Optimization for Load Balancing in Cloud Computing

Online veröffentlicht: 17. Aug. 2023

Seitenbereich: 170 - 179

DOI: https://doi.org/10.2478/acss-2023-0017

SchlüsselwörterGrey wolf optimization, inquisitive genetic algorithm, load balancing, task allocation, virtual machine

© 2023 Suman Sansanwal et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Grey wolf optimization, inquisitive genetic algorithm, load balancing, task allocation, virtual machine