Advanced Artificial Intelligence Techniques for Cloud Com- puting Optimization: A Comprehensive Analysis of AI-Driven Resource Management, Performance Enhancement, and Fault Tolerance Mechanisms

Abstract

The rapid evolution of cloud computing necessitates advanced management strategies to optimize performance, resource allocation, and fault tolerance in increasingly complex cloud environments. Artificial Intelligence (AI) plays a critical role in automating and enhancing these management tasks, offering innovative solutions to challenges such as load balancing, fault management, energy efficiency, and security. This paper provides a comprehensive analysis of AI-driven methodologies and their impact on cloud computing. We explore AI-assisted load prediction models, virtualization techniques, proactive fault management systems, and resource provisioning strategies, highlighting their contributions to optimizing cloud performance. Furthermore, we investigate how AI techniques are employed to achieve energy-efficient operations, minimize downtime, and enhance the quality of service (QoS) across diverse cloud architectures. The study also delves into the role of machine learning in secure cloud environments, the application of deep learning for predictive maintenance, and the impact of evolutionary algorithms on cloud resource allocation. By synthesizing findings from recent research, this paper aims to present a holistic view of the current state of AI in cloud computing, identify the gaps in existing approaches, and suggest potential directions for future developments. Through extensive literature review and analysis, this work underscores the transformative potential of AI in cloud computing, paving the way for more intelligent, adaptive, and resilient cloud infrastructures that can better meet the dynamic demands of modern digital ecosystems. This study will serve as a valuable resource for researchers and practitioners aiming to leverage AI for cloud optimization.