AI-Driven Cloud Optimization : Transforming Modern Infrastructure Management
Year: 2025
Type: article
Abstract: This article explores how AI-driven cloud optimization is transforming modern infrastructure management by enabling organizations to maximize their cloud investments while maintaining optimal performance. The convergence of artificial intelligence, machine learning, and cloud computing technologies has created systems capable of analyzing operational patterns, predicting resource requirements, and automatically adjusting cloud configurations without human intervention. It examines five key benefits of AI-driven optimization: cost reduction through intelligent resource allocation, performance enhancement via dynamic resource management, intelligent scalability through predictive capacity planning, operational automation that reduces IT burden, and environmental sustainability through efficient resource utilization. The article further analyzes three implementation approaches—cloud provider native tools, third-party optimization platforms, and custom AI solutions—while discussing critical technical considerations including data collection infrastructure, AI/ML model selection, integration requirements, and governance frameworks. The article concludes by examining emerging trends such as autonomous operations, cross-layer optimization, and quantum-enhanced optimization that will shape the future of cloud resource management and deliver even greater efficiency, performance, and business value.
Author: Ramamohan Kummara
Institution: Indian Institute of Technology Hyderabad
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408477828