PREDICTIVE CAPACITY PLANNING USING MACHINE LEARNING FOR ENTERPRISE APPLICATIONS

Abstract

Capacity planning for enterprise applications has traditionally relied on rule-of-thumb heuristics, static provisioning based on peak estimates, and reactive scaling that responds to problems only after they manifest as performance degradation. These approaches lead to either costly over-provisioning or risky under-provisioning, and neither adapts well to the increasingly dynamic and unpredictable workloads of modern enterprise systems. This paper presents a machine learning framework for predictive capacity planning that forecasts resource demand ahead of time, enabling proactive provisioning that balances cost and performance. We designed a forecasting system combining time-series models for baseline demand prediction with gradient boosted models for incorporating business drivers, and evaluated it against conventional capacity planning approaches using 18 months of production telemetry from a portfolio of enterprise applications. Results show that the ML framework reduced resource over-provisioning by 32% while simultaneously reducing capacity-related performance incidents by 58% compared to static peak-based provisioning. Forecast accuracy for CPU and memory demand achieved mean absolute percentage errors of 11.4% and 9.7% respectively at a two-week horizon. The framework's incorporation of business event calendars improved forecast accuracy during high-variance periods such as month-end processing and marketing campaigns by 27%. The paper discusses the practical integration of predictive capacity planning into enterprise operations, including the organizational shift from reactive to proactive capacity management, and addresses the challenges of forecast uncertainty, cold-start for new applications, and model maintenance.