AI-DRIVEN OPTIMIZATION TECHNIQUES FOR CLOUD COMPUTING: ENHANCING PERFORMANCE, EFFICIENCY, AND RELIABILITY

Abstract

The rapid evolution of cloud computing has led to significant advancements in the management of cloud resources, driven primarily by the integration of Artificial Intelligence (AI) techniques. AI's role in enhancing cloud performance, improving resource allocation, and optimizing operational efficiency has become indispensable in the face of growing data volumes and complex computational needs. This paper provides a comprehensive analysis of various AI-driven techniques employed to optimize cloud computing environments. Key areas explored include load prediction, virtualization, fault management, and energy-efficient resource allocation. We delve into predictive models that enhance fault tolerance, energy consumption strategies that maintain high reliability, and advanced scheduling algorithms for efficient task management. The paper synthesizes research findings on AI-assisted virtualization, proactive maintenance, and security mechanisms in cloud systems, highlighting the importance of AI in scaling and adapting to dynamic cloud environments. This synthesis aims to provide a holistic understanding of how AI methodologies are revolutionizing cloud computing, ultimately driving towards more scalable, reliable, and efficient cloud services. The discussion is supported by extensive citations of existing research, outlining the theoretical and practical contributions of AI to cloud computing.