Year: 2025
Type: article
Abstract: In today’s rapidly evolving digital landscape, demand forecasting and capacity planning have become critical for the efficient management of AI and cloud-based infrastructure solutions. The exponential growth in data generation and the increasing reliance on artificial intelligence have compelled organizations to adopt innovative predictive analytics to ensure that infrastructure resources meet current and future demands. This study explores a range of methodologies that combine statistical analysis with machine learning techniques to accurately predict workload trends and infrastructure needs. By analyzing historical data, market trends, and seasonal patterns, businesses can identify both short-term fluctuations and long-term growth trajectories. This approach minimizes the risk of resource underutilization and overprovisioning, leading to cost savings and enhanced operational performance. The integration of real-time data analytics further refines these forecasts, providing a dynamic feedback loop that adjusts capacity in response to sudden market changes or technological advancements. Ultimately, the study presents a robust framework that supports agile and adaptive resource management, ensuring that cloud-based infrastructures are scalable, resilient, and capable of supporting the complex demands of modern AI applications. This comprehensive approach not only optimizes operational efficiency but also empowers organizations to maintain a competitive edge in a rapidly transforming technological ecosystem
Authors: Sattvik Sharma, Er. Kratika Jain
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414829827