Speaker
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.
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