Machine Learning and AI Strategies for Enhancing Cloud Computing Efficiency

Abstract

The integration of Machine Learning (ML) and Artificial Intelligence (AI) into cloud computing frameworks represents a transformative shift towards enhanced operational efficiency and resource management. This paper explores advanced ML and AI strategies designed to optimize cloud computing environments by improving performance, scalability, and cost-effectiveness. We investigate various AI-driven techniques, including predictive analytics, autonomous resource allocation, and adaptive optimization, to address common challenges such as resource overprovisioning, performance bottlenecks, and energy inefficiencies. By analyzing case studies and experimental results, we demonstrate how these strategies contribute to a more dynamic, responsive, and economical cloud infrastructure. The findings indicate that incorporating ML and AI can lead to substantial improvements in cloud efficiency, including reduced operational costs, enhanced system reliability, and improved user experiences. This research provides a comprehensive overview of current trends, methodologies, and practical applications of AI and ML in cloud computing, offering valuable insights for both practitioners and researchers aiming to leverage these technologies for optimizing cloud services.