Advanced AI Techniques in Cloud Computing: Enhancing Performance, Security, and Resource Management through Intelligent Automation and Optimization

Abstract

Artificial Intelligence (AI) has revolutionized cloud computing by introducing intelligent automation, performance optimization, and advanced fault-tolerance strategies. This paper comprehensively explores AI-driven methodologies that address the challenges of cloud resource management, predictive maintenance, fault tolerance, and security in cloud environments. We delve into various AI-enhanced techniques, including machine learning models, deep learning algorithms, and evolutionary approaches, to optimize resource allocation, improve cloud service reliability, and enhance security. AI-driven load prediction and fault management strategies offer predictive capabilities that significantly enhance cloud elasticity and fault tolerance, minimizing energy consumption while maintaining high reliability and quality of service. Furthermore, AI's role in enhancing cloud security through predictive analytics and anomaly detection models is evaluated. We analyze the comparative efficiency of heuristic and AI-based task scheduling algorithms in cloud and fog computing, emphasizing their impact on latency, energy efficiency, and scalability in dynamic, heterogeneous environments. The interplay between AI-driven predictive analytics and cloud orchestration is also discussed, highlighting how AI models can dynamically optimize quality of service (QoS) parameters across multi-cloud environments. Finally, this paper examines the potential of AI for cost optimization, data-driven decision-making, and proactive management strategies in cloud computing, presenting a future outlook on how AI can drive further advancements in this field. This review synthesizes findings from a broad range of studies to provide an integrated perspective on the role of AI in transforming cloud computing landscapes, ultimately enhancing the efficiency, security, and scalability of cloud services across diverse applications