Performance Modeling of Data Storage Systems using Generative Models

IEEE Access. 2025. Vol. 13. P. 49643–49658.

Al-Maeeni A. R., Temirkhanov A., Ryzhikov A., Hushchyn M.

High-precision systems modeling is one of the main areas of industrial data analysis. Models of systems, their digital twins, are used to predict their behavior under various conditions. In this study, we developed several models of a storage system using machine learning-based generative models to predict performance metrics such as IOPS and latency. The models achieve prediction errors ranging from 4%–10% for IOPS and 3%–16% for latency and demonstrate high correlation (up to 0.99) with observed data. By leveraging Little’s law for validation, these models provide reliable performance estimates. Our results outperform conventional regression methods, offering a vendor-agnostic approach for simulating data storage system behavior. These findings have significant applications for predictive maintenance, performance optimization, and uncertainty estimation in storage system design.

Keywords: машинное обучение, machine learning, цифровой двойник, digital twins

Publication based on the results of: Повышение эффективности центров обработки данных и систем хранения данных методами искусственного интеллекта (2027)