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Artificial Intelligence Driven Cloud Automation: Architecturing Intelligent Infrastructure Operations
Subject area: Science,Engineering and Technology · Area of research: AI-Driven Cloud Automation
DOI: 10.64388/IREV10I3-1722814
Abstract
Cloud infrastructure has grown too complex for traditional manual and rule-based management, leading to inefficiencies and scalability challenges. This paper proposes an AI-driven framework that combines machine learning, Infrastructure as Code (IaC), and AIOps within a microservices architecture to enable intelligent and autonomous infrastructure operations. Developed using the Rapid Application Development (RAD) methodology, the framework emphasizes adaptability, iterative refinement, and user-centered design. Experimental results show improved anomaly detection accuracy, faster incident response, reduced downtime, and lower operational costs compared to traditional management approaches. The work contributes a scalable blueprint that bridges theoretical AI models with practical enterprise cloud automation, paving the way for resilient, self-improving, and future-ready IT environments.
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How to cite this paper
@article{1722814,
author = {Niemogha Star Umiyeromesu, Evwarhono Akpevwe Courage, Onokpasa Aghogho},
title = {Artificial Intelligence Driven Cloud Automation: Architecturing Intelligent Infrastructure Operations},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {3},
pages = {655-665},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722814.pdf},
abstract = {Cloud infrastructure has grown too complex for traditional manual and rule-based management, leading to inefficiencies and scalability challenges. This paper proposes an AI-driven framework that combines machine learning, Infrastructure as Code (IaC), and AIOps within a microservices architecture to enable intelligent and autonomous infrastructure operations. Developed using the Rapid Application Development (RAD) methodology, the framework emphasizes adaptability, iterative refinement, and user-centered design. Experimental results show improved anomaly detection accuracy, faster incident response, reduced downtime, and lower operational costs compared to traditional management approaches. The work contributes a scalable blueprint that bridges theoretical AI models with practical enterprise cloud automation, paving the way for resilient, self-improving, and future-ready IT environments.},
month = {September},
doi = {https://doi.org/10.64388/IREV10I3-1722814}
}