Dr. Tharushi Jayawardena
1
Dr. Pasindu Rathnayake
1
4
Department of Computer Science Royal Bhutan Institute of Technology Thimphu, Bhutan
4
Faculty of Engineering Sciences Central Island Technical University
Abstract
Cloud infrastructure automation has emerged as a foundational component of modern enterprise computing environments due to the increasing complexity of distributed systems, virtualization technologies, and dynamic service delivery requirements. Organizations are rapidly transitioning from manually managed infrastructures to intelligent and automated cloud ecosystems capable of supporting scalability, operational efficiency, security, and rapid deployment cycles. This research paper presents a comprehensive framework for cloud infrastructure automation by analyzing emerging technologies, integration strategies, and future research trends influencing modern cloud architectures. The study critically evaluates infrastructure as code (IaC), container orchestration, continuous integration and deployment pipelines, serverless computing, cloud orchestration engines, and artificial intelligence-driven automation mechanisms. A detailed literature synthesis based exclusively on existing scholarly references identifies current implementation models, technical limitations, operational challenges, and strategic opportunities. The proposed framework integrates automation layers including provisioning, orchestration, monitoring, security governance, workload balancing, and intelligent decision support within cloud ecosystems. Furthermore, the paper examines interoperability challenges, configuration management concerns, scalability optimization, and security implications associated with cloud automation frameworks. Results indicate that infrastructure automation significantly improves deployment consistency, operational agility, and resource utilization while reducing human intervention and system downtime. However, challenges related to vendor lock-in, integration complexity, configuration drift, and policy governance continue to hinder full-scale adoption. The study contributes a structured analytical model for understanding cloud automation evolution and provides future research directions emphasizing AI-assisted orchestration, hybrid cloud governance, autonomous infrastructure management, and adaptive security automation.
How to Cite
Dr. Tharushi Jayawardena, & Dr. Pasindu Rathnayake. (2026). A Comprehensive Framework for Cloud Infrastructure Automation: Emerging Technologies, Integration Strategies, and Future Research Trends. Frontiers in Emerging Artificial Intelligence and Machine Learning, 3(04), 01–10. Retrieved from https://irjernet.com/index.php/feaiml/article/view/398
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