Authors: Suresh Perera
Abstract: The increasing complexity and scale of cloud infrastructure have necessitated the adoption of advanced automation techniques to ensure efficient management, reliability, and cost optimization. Artificial Intelligence (AI)-driven automation has emerged as a transformative approach, enabling cloud environments to become more adaptive, self-managing, and resilient. This study explores the integration of AI technologies—such as machine learning, deep learning, and predictive analytics—into cloud infrastructure management processes, including resource provisioning, workload balancing, fault detection, and performance optimization. The paper examines how AI-driven systems can analyze large volumes of operational data generated by cloud platforms to identify patterns, predict potential failures, and automate decision-making processes in real time. It highlights the role of intelligent orchestration tools, autonomous scaling mechanisms, and anomaly detection systems in enhancing operational efficiency and reducing human intervention. Additionally, the study discusses the integration of AI with DevOps practices, enabling continuous monitoring, automated deployment, and self-healing infrastructure. Key challenges such as data privacy, model accuracy, integration complexity, and skill gaps are critically analyzed, along with strategies to address them. The findings emphasize that AI-driven automation significantly improves the scalability, reliability, and cost-effectiveness of cloud infrastructure management. As cloud environments continue to evolve, the adoption of intelligent automation will be essential for organizations seeking to maintain agility, optimize resources, and achieve sustainable digital transformation.
DOI: https://doi.org/10.5281/zenodo.19679914
