Optimizing Cloud Computing Performance through Artificial Intelligence and Machine Learning Techniques

TITLE	Optimizing Cloud Computing Performance through Artificial Intelligence and Machine Learning Techniques
ABSTRACT	Cloud computing has revolutionized modern computing by providing scalable, on-demand, and cost-effective access to computational resources. However, as cloud infrastructure becomes more complex, ensuring optimal performance remains a critical challenge. Artificial Intelligence (AI) and Machine Learning (ML) techniques offer promising solutions to this challenge by enabling intelligent resource management, workload prediction, anomaly detection, and automated decision-making. This paper explores the integration of AI/ML in optimizing cloud computing performance, focusing on real-time monitoring, dynamic resource allocation, auto-scaling, and fault tolerance. We provide an in-depth review of recent literature on AI/ML-driven optimization techniques, highlighting their strengths, limitations, and practical applications in various cloud environments such as public, private, and hybrid clouds. A research methodology based on simulation and real-world case studies is proposed to evaluate the effectiveness of AI/ML approaches. The study identifies significant performance gains in terms of reduced latency, improved throughput, enhanced reliability, and cost efficiency. Additionally, we examine various ML models like reinforcement learning, deep neural networks, and supervised learning algorithms that are commonly employed for cloud optimization tasks. The results of our analysis reveal that AI/ML techniques can enhance decision-making in resource provisioning and workload balancing, particularly under dynamic and unpredictable conditions. This paper concludes with a discussion of current challenges, such as data privacy, model interpretability, and integration complexity. Furthermore, future research directions are proposed to enhance the synergy between AI and cloud computing, including federated learning, explainable AI, and self-adaptive cloud systems. Ultimately, this research contributes to building more intelligent, resilient, and autonomous cloud infrastructures.
AUTHOR	Manjiri Prabhu Department of Information Technology, Dr. D. Y. Patil College of Engineering and Innovation, Pune, India
VOLUME	12
DOI	DOI:10.15680/IJARETY.2025.1206010
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