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2020 | OriginalPaper | Chapter

Artificial Neural Network Based Load Balancing in Cloud Environment

Authors : Sarita Negi, Neelam Panwar, Kunwar Singh Vaisla, Man Mohan Singh Rauthan

Published in: Advances in Data and Information Sciences

Publisher: Springer Singapore

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Abstract

With heavy demand for cloud technology, it is important to balance the cloud load to deliver seamless Quality of Services to the different cloud users. To address such issues, a new hybridized technique Artificial Neural Network based Load Balancing (ANN-LB) is introduced to calculate an optimized Virtual Machine (VM) load in cloud systems. The Particle Swarm Optimization (PSO) technique is used to perform task scheduling. The performance of the proposed ANN-LB approach has been analyzed with the existing CM-eFCFS, Round Robin, MaxMin, and MinMin algorithms based on MakeSpan, Average Resource Utilization, and Transmission Time. Calculated values and plotted graphs illustrate that the presented work is efficient and effective for load balancing. Hybridization of ANN and iK-mean methods obtains a proper load balancing among VMs and results have been remarkable.

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Metadata
Title
Artificial Neural Network Based Load Balancing in Cloud Environment
Authors
Sarita Negi
Neelam Panwar
Kunwar Singh Vaisla
Man Mohan Singh Rauthan
Copyright Year
2020
Publisher
Springer Singapore
Book
Advances in Data and Information Sciences

Print ISBN: 978-981-15-0693-2

Electronic ISBN: 978-981-15-0694-9

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-15-0694-9_20