AI-Enhanced Capacity Planning for Cloud Infrastructure

Abstract

In the constantly changing world of cloud computing, AI-enhanced Capacity Planning for Cloud Infrastructure has become a crucial field with the goals of optimizing resource usage, lowering operating costs, and enhancing service reliability. This study addresses the difficulties brought on by fluctuating workloads and dynamic resource needs by investigating the integration of innovative technologies. AI and machine learning techniques to improve capacity planning for cloud settings. The paper methodically examines various AI-driven techniques for resource forecasting, adaptive cloud infrastructure management, and predictive analytics. The project aims to predict workload patterns, optimize resource allocation, and reduce potential performance bottlenecks using neural networks, machine learning models, and big data analytics. The process includes putting predictive algorithms into practice, assessing performance, and contrasting AI-enhanced capacity planning models with conventional techniques. The findings show that AI-based methods increase workload prediction and resource management accuracy, which lowers costs and improves system performance. The results highlight AI's ability to build more robust and effective cloud environments, which has ramifications for cloud service providers and businesses looking for intelligent, scalable infrastructure solutions. Future research attempts to investigate real-time adaptive ways to react to changing cloud dynamics and improve AI models for increased precision.