A Dynamic Scalable Auto-Scaling Model as a Load Balancer in the Cloud Computing Environment

Saroja Kumar Rout; JVR Ravinda; Anudeep Meda; Sachi Nandan Mohanty; Venkatesh Kavididevi

doi:10.4108/eetsis.3356

Authors

Saroja Kumar Rout Vardhaman College of Engineering https://orcid.org/0000-0001-9007-3665
JVR Ravinda Vardhaman College of Engineering https://orcid.org/0000-0001-7636-1791
Anudeep Meda Vardhaman College of Engineering https://orcid.org/0000-0003-1194-4579
Sachi Nandan Mohanty Vellore Institute of Technology University https://orcid.org/0000-0002-4939-0797
Venkatesh Kavididevi Vardhaman College of Engineering https://orcid.org/0000-0003-0964-7874

DOI:

https://doi.org/10.4108/eetsis.3356

Keywords:

Cloud Computing, Auto-Scaling, Virtualization, Virtual Machine

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.

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