AI-Driven Capacity Planning in Large-Scale Infrastructure: A Comparative Analysis of LSTM Networks and Traditional Forecasting Methods

Abstract

Infrastructure capacity planning presents significant challenges in modern distributed systems, where traditional forecasting methods often fail to accommodate dynamic resource demands effectively. This article examines the implementation of artificial intelligence-driven capacity planning systems, specifically focusing on Long Short-Term Memory (LSTM) networks and advanced machine learning algorithms, across large-scale infrastructure deployments. Through a mixed-methods approach combining quantitative analysis of historical performance data from multiple enterprise deployments and detailed case studies of implementations at Netflix and Microsoft Azure, we demonstrate that AI-driven planning systems achieve a 47% improvement in prediction accuracy compared to traditional methods, while reducing resource over-provisioning by 31%. The findings indicate that LSTM-based models excel particularly in environments with irregular usage patterns, achieving 93% prediction accuracy in highly variable workloads compared to 76% for conventional statistical methods. The integration of cross-layer monitoring with 5G technologies introduces transformative capabilities, enabling more sophisticated resource optimization with a 45% improvement in overall resource utilization. The article also reveals significant improvements in educational environments, with Azure's implementation demonstrating a 35% reduction in per-student costs and 64% increase in system utilization. Additionally, our investigation addresses critical security and privacy considerations in AI-driven systems, providing a comprehensive framework for privacy-preserving technology implementation and industry adoption projections. These results suggest that AI-driven capacity planning represents a significant advancement in infrastructure management, offering improved operational efficiency, substantial cost benefits, and enhanced security measures across diverse operational contexts.