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Quarterly Journal of Emerging Technologies and Innovations

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Architecture of the Integrated Analytics Module demonstrating machine learning workflows with real-time monitoring and optimization feedback loops. PDF

Published

2023-07-16

Issue

Vol. 8 No. 2 (2023): QJETI-APRIL-JUNE

Section

Articles

How to Cite

Kansara, M. (2023). A Framework for Automation of Cloud Migrations for Efficiency, Scalability, and Robust Security Across Diverse Infrastructures. Quarterly Journal of Emerging Technologies and Innovations, 8(2), 173–189. Retrieved from https://vectoral.org/index.php/QJETI/article/view/175

A Framework for Automation of Cloud Migrations for Efficiency, Scalability, and Robust Security Across Diverse Infrastructures

Maheshbhai Kansara

Engineering Manager, Amazon Web Services.

Abstract

Migration processes used to be manual and labor-intensive, with results that often involve extended downtime, inconsistent data states, and heightened exposure to misconfiguration risks. As a solution to these problems, automation has emerged as a game-changing strategy to streamline and optimize the migration process. Automation not only reduces human error but also allows for rapid scaling and adaptation to workload demands. Automating cloud migration not only speeds up the deployment process but also removes the risk of human error while optimizing the allocation of resources.This research work proposes a framework that automates the entire process of cloud migration by integrating advanced orchestration mechanisms, containerization approaches, and machine learning–driven predictive analytics. The proposed system is structured around a firm abstraction layer between application dependencies and hardware infrastructure, for facilitating effortless accommodation of heterogeneous cloud environments. Task sequencing, real-time performance monitoring, and automated rollback processes are handled in the dynamic orchestration engine with minimal disruption to services. This is supported by the analytics module that constantly analyzes system metrics, predicts bottlenecks, and optimizes resource allocation during the migration process. Experiments on multi-cloud simulated environments demonstrate a reduction in migration time and downtime, with improved data integrity and compliance with stringent security policies. The study offers actionable information on the integration of automation into cloud migration strategies, reporting on a scalable and fault-tolerant solution to underpin digital transformation initiatives. The findings show the main contribution of automation to legacy infrastructure transformation without sacrificing high performance and security levels.

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Submission Guidelines for the Journal of Quarterly Journal of Emerging Technologies and Innovations (QJETI) General Guidelines:

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Published by Vectoral Publishing, the Quarterly Journal of Emerging Technologies and Innovations maintains high standards in peer-review and scientific inquiry. Authors who wish to submit manuscripts should adhere to the guidelines detailed on our website, www.vectoral.org/QJETI. Vectoral Publishing does not bear any responsibility for the accuracy of information or for any outcomes related to the use of the research published. For editorial correspondence and submissions, please reach out to editorial@vectoral.org.