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
Publication history: 
Received on 01 November 2023; revised on 10 December 2023; accepted on 12 December 2023
 
Abstract: 
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.
 
Keywords: 
Artificial Intelligence; AWS Optimization; Cloud Performance; Machine Learning; Predictive Analytics; Auto-Scaling; Resource Management; And Anomaly Detection
 
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0