Optimizing Cloud Resource Allocation with Machine Learning: A Comprehensive Approach to Efficiency and Performance

Abstract Cloud computing has become a cornerstone of modern IT infrastructure, and effective resource management is essential for maximizing performance and minimizing costs. This paper explores the application of machine learning algorithms to optimize cloud resource management. We utilize datasets that capture key performance metrics such as CPU usage, memory consumption, and network traffic. Our methodology involves preprocessing and analysing these datasets to develop predictive and optimization models aimed at improving resource allocation and efficiency. We apply various machine learning techniques, including regression analysis, reinforcement learning, and clustering, to address challenges related to resource utilization and cost management. The results are visualized through performance metrics graphs, heatmaps, and comparison charts that illustrate the impact of our models on cloud resource optimization. Interactive dashboards are also employed to provide real-time insights into resource management improvements. This research highlights the intersection of AI and cloud computing, demonstrating how machine learning can significantly enhance the efficiency and effectiveness of cloud resource management.