AI-Driven Capacity Planning in Large-Scale Infrastructure: A Comparative Analysis of LSTM Networks and Traditional Forecasting Methods
Year: 2024
Type: article
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.
Author: Saikiran Rallabandi
Institution: Apple (United States)
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404854247