AI-Enhanced Cloud Automation: A Framework for Next-Generation Infrastructure Management

Authors

  • Ganesh Vanam Zebra Technologies Corporation, USA Author

DOI:

https://doi.org/10.32628/CSEIT25111204

Keywords:

Cloud Infrastructure Automation, Artificial Intelligence in DevOps, Autonomous Resource Management, Intelligent Orchestration Systems, Infrastructure AIOps

Abstract

The integration of artificial intelligence with cloud automation represents a paradigm shift in infrastructure management, offering organizations unprecedented capabilities to optimize and maintain complex IT environments. This article examines the transformative impact of AI-driven cloud automation, focusing on three key innovations: predictive scaling mechanisms, autonomous remediation systems, and intelligent container orchestration. Through analysis of current implementations across major cloud platforms and industry-leading AIOps tools, the article explores how these technologies are revolutionizing resource management, incident response, and operational efficiency. The article draws insights from implementations in healthcare and financial services sectors, demonstrating tangible improvements in system reliability, cost optimization, and innovation acceleration. The article findings suggest that AI-driven cloud automation not only enhances traditional infrastructure management practices but also enables organizations to build more resilient, scalable, and intelligent systems that can adapt to dynamic workload requirements while minimizing human intervention. This article contributes to the growing body of knowledge on intelligent infrastructure management and provides practical insights for organizations seeking to leverage AI capabilities in their cloud environments.

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Published

03-01-2025

Issue

Section

Research Articles

How to Cite

AI-Enhanced Cloud Automation: A Framework for Next-Generation Infrastructure Management. (2025). International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(1), 12-19. https://doi.org/10.32628/CSEIT25111204