Research Article
A Dynamic Scalable Auto-Scaling Model as a Load Balancer in the Cloud Computing Environment
@ARTICLE{10.4108/eetsis.3356, author={Saroja Kumar Rout and JVR Ravinda and Anudeep Meda and Venkatesh Kavididevi}, title={A Dynamic Scalable Auto-Scaling Model as a Load Balancer in the Cloud Computing Environment}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={10}, number={5}, publisher={EAI}, journal_a={SIS}, year={2023}, month={7}, keywords={Cloud Computing, Auto-Scaling, Virtualization, Virtual Machine}, doi={10.4108/eetsis.3356} }- Saroja Kumar Rout
JVR Ravinda
Anudeep Meda
Venkatesh Kavididevi
Year: 2023
A Dynamic Scalable Auto-Scaling Model as a Load Balancer in the Cloud Computing Environment
SIS
EAI
DOI: 10.4108/eetsis.3356
Abstract
INTRODUCTION: Cloud services are becoming increasingly important as advanced technology changes. In these kinds of cases, the volume of work on the corresponding server in public real-time data virtualized environment can vary based on the user’s needs. Cloud computing is the most recent technology that provides on-demand access to computer resources without the user’s direct interference. Consequently, cloud-based businesses must be scalable to succeed. OBJECTIVES: The purpose of this research work is to describe a new virtual cluster architecture that allows cloud applications to scale dynamically within the virtualization of cloud computing scale Using auto-scaling, resources can be dynamically adjusted to meet multiple demands. METHODS: An auto-scaling algorithm based on the current implementation sessions will be initiated for automated provisioning and balancing of virtualized resources. The suggested methodology also considers the cost of energy. RESULTS: The proposed research work has shown that the suggested technique can handle sudden load demands while maintaining higher resource usage and lowering energy costs efficiently. CONCLUSION: Auto-scaling features are available in measures in order groups, allowing you to automatically add or remove instances from a managed instance group based on changes in load. This research work provides an analysis of auto-scaling mechanisms in cloud services that can be used to find the most efficient and optimal solution in practice and to manage cloud services efficiently.
Copyright © 2023 Rout et al., licensed to EAI. This is an open access article distributed under the terms of the CC BYNC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
