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May 8 – 12, 2023
Norfolk Waterside Marriott
US/Eastern timezone

Digital Twin of a Data Storage System based on Generative Models

Not scheduled
1h
Hampton Roads Ballroom and Foyer Area (Norfolk Waterside Marriott)

Hampton Roads Ballroom and Foyer Area

Norfolk Waterside Marriott

235 East Main Street Norfolk, VA 23510
Poster Poster Poster Session

Speaker

Ratnikov, Fedor (HSE University)

Description

High-precision modeling of systems is one of the main areas of industrial data analysis today. Models of the systems, their digital twins, are used to predict their behavior under various conditions. We have developed a digital twin of a data storage system using generative models of machine learning. The system consists of several types of components: HDD and SSD disks, disk pools with different RAID schemas, and cache. We represent each component by a probabilistic model that describes the probability distribution of component performance values depending on their configuration and external data load parameters. Machine learning helps to get a highly accurate digital twin of a particular system, spending less time and resources than other analogues. It quickly predicts the performance of the system, which significantly speeds up the development of new data storage systems. Also, comparing the forecasts of the digital twin with the real performance helps to diagnose failures and anomalies in the system, increasing its reliability.

Consider for long presentation No

Primary authors

Hushchyn, Mikhail (HSE University) Mr Al-Maeeni, Abdalaziz (HSE University) Mr Temirkhanov, Aziz (HSE University) Mr Ryzhikov, Artem (HSE University) Mr Maevskiy, Dmitry (HSE University) Pakhomov, Yuri (HSE University) Ratnikov, Fedor (HSE University)

Presentation materials

Peer reviewing

Paper