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ISSN Approved Journal || eISSN: 2582-8185 || CODEN: IJSRO2 || Impact Factor 8.2 || Google Scholar and CrossRef Indexed

Peer Reviewed and Referred Journal || Free Certificate of Publication

Research and review articles are invited for publication in March 2026 (Volume 18, Issue 3) Submit manuscript

AI-powered insights for performance optimization in AWS cloud environments

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  • AI-powered insights for performance optimization in AWS cloud environments

Venkata Ramana Gudelli *

Independent Researcher, Brambleton, Virgina, USA.

Research Article

 

International Journal of Science and Research Archive, 2023, 10(02), 1267-1276.
Article DOI: 10.30574/ijsra.2023.10.2.1033
DOI url: https://doi.org/10.30574/ijsra.2023.10.2.1033

Received on 01 November 2023; revised on 10 December 2023; accepted on 12 December 2023

Enterprise IT infrastructures experience a transformation because of rapid cloud computing adoption, especially Amazon Web Services (AWS), which delivers scalability, flexibility, and cost efficiency. AWS users encounter difficulties optimizing their performance because they face problematic situations combining workload variability with complex resource distribution systems and delay issues. The implementation of Artificial Intelligence constructs an evaluation process to boost performance optimization in Amazon Web Services (AWS) cloud systems. AI-powered insights implemented through machine learning algorithms with predictive analytics capabilities and automation functionality improve workload distribution and enhance auto-scaling efficiency while reducing operational expenses. Processor analysis of historical data combined with real-time metrics allows AI algorithms to make proactive decisions about resource administration, detect anomalies, and perform innovative management. The research evaluates performance-altering AI-based approaches that enhance AWS services, including EC2, Lambda, and Sage Maker. Implementing AI-based optimizations generates copious improvements in computational efficiency and cost performance based on experimental data. Thanks to AI, cloud performance management strategies will fundamentally transform since they endorse efficient AWS environments that align with business requirements.

Artificial Intelligence; AWS Optimization; Cloud Performance; Machine Learning; Predictive Analytics; Auto-Scaling; Resource Management; And Anomaly Detection

https://ijsra.net/sites/default/files/fulltext_pdf/IJSRA-2023-1033.pdf

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Venkata Ramana Gudelli. Cost optimization strategies for micro services in AWS: Managing resource consumption and scaling efficiently. International Journal of Science and Research Archive, 2023, 10(02), 1267-1276. Article DOI: https://doi.org/10.30574/ijsra.2023.10.2.1033

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.


All statements, opinions, and data contained in this publication are solely those of the individual author(s) and contributor(s). The journal, editors, reviewers, and publisher disclaim any responsibility or liability for the content, including accuracy, completeness, or any consequences arising from its use.

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