AI-Powered Predictive Analytics for Dynamic Cloud Resource Optimization: A Technical Implementation Framework - Siddharth Kumar Choudhary (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
This article explores the transformative impact of AI-driven predictive analytics on cloud resource optimization, presenting a comprehensive technical implementation framework. The article shows how artificial intelligence and machine learning architectures revolutionize traditional cloud management approaches through advanced predictive capabilities and dynamic resource allocation. It examines the evolution from static threshold-based systems to sophisticated AI-driven solutions, analyzing their implementation strategies across various organizational contexts. The article delves into multiple optimization domains, including capacity provisioning, cost management, performance enhancement, and energy efficiency, while presenting real-world applications and impact analyses across different industries. Through extensive case studies and empirical evidence, the article demonstrates how organizations leverage AI-powered solutions to address complex cloud resource management challenges, achieve operational efficiencies, and maintain competitive advantages in the digital marketplace. The article also explores future developments and provides strategic recommendations for organizations implementing cloud optimization frameworks, emphasizing the importance of standardized approaches, stakeholder engagement, and sustainable practices in cloud resource management.