Work

Artificial Intelligence Driven Cloud Automation: Architecturing Intelligent Infrastructure Operations

Year: 2026

Type: article

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.

Source: Iconic Research and Engineering Journals

DOI: 10.64388/irev10i3-1722814

Authors: Niemogha Star Umiyeromesu, Evwarhono Akpevwe Courage, Onokpasa Aghogho

Institution: Federal University of Petroleum Resource Effurun

Language: English

Topic: Software System Performance and Reliability

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

OpenAlex ID: W7212065892