Intelligent Performance Capacity Planning: AI-Powered Approaches for Cloud, Hybrid, and Distributed Systems

Abstract

Performance capacity planning has traditionally been essential for ensuring that computing infrastructures meet changing workload requirements while maintaining reliability, efficiency, and cost-effectiveness. Conventional approaches such as trend analysis, rule-based forecasting, and manual resource evaluation are increasingly inadequate due to the complexity of cloud, hybrid, and distributed computing environments. Artificial intelligence (AI) provides a more accurate, flexible, and data-driven solution to these challenges. This study proposes an AI-based capacity planning framework that integrates machine learning, predictive analytics, anomaly detection, and real-time monitoring to continuously evaluate infrastructure performance, predict resource requirements, and optimize capacity allocation. The framework uses operational data to improve forecasting accuracy, support proactive decision-making, reduce resource wastage, and enhance system responsiveness and resilience. Evaluation across a multi-environment computing infrastructure demonstrates that AI-based capacity planning responds more effectively to fluctuating workloads than traditional methods. The study contributes to intelligent infrastructure management by presenting a scalable approach that enables organizations to transition from reactive capacity management toward predictive and autonomous planning. Future developments may incorporate advanced deep learning, autonomous resource management, and self-optimizing infrastructures for next-generation computing environments.