Leveraging Data Science for Intelligent Cloud Infrastructure Management

Abstract

The rapid evolution of cloud computing has significantly transformed how organizations manage and utilize their IT resources. This paper explores the application of data science techniques for enhancing cloud infrastructure management, focusing on optimizing resource allocation, improving system performance, and ensuring operational efficiency. We present a comprehensive framework that leverages advanced data analytics, machine learning, and predictive modeling to address key challenges in cloud infrastructure management, including resource provisioning, workload balancing, and fault detection. Our framework integrates data from various sources, including system logs, usage metrics, and performance indicators, to develop intelligent models that forecast demand, detect anomalies, and optimize resource utilization. We demonstrate the effectiveness of our approach through a series of case studies and simulations, showcasing significant improvements in operational efficiency, cost savings, and system reliability. The results indicate that data-driven insights can substantially enhance cloud infrastructure management by enabling proactive and adaptive strategies. This paper contributes to the growing body of knowledge on leveraging data science in cloud environments, providing practical solutions for optimizing infrastructure management and paving the way for more intelligent and responsive cloud systems.