  High-precision modeling of systems 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. We have developed several models of a
storage system using machine learning-based generative models. The system
consists of several components: hard disk drive (HDD) and solid-state drive
(SSD) storage pools with different RAID schemes and cache. Each storage
component is represented by a probabilistic model that describes the
probability distribution of the component performance in terms of IOPS and
latency, depending on their configuration and external data load parameters.
The results of the experiments demonstrate the errors of 4-10 % for IOPS and
3-16 % for latency predictions depending on the components and models of the
system. The predictions show up to 0.99 Pearson correlation with Little's law,
which can be used for unsupervised reliability checks of the models. In
addition, we present novel data sets that can be used for benchmarking
regression algorithms, conditional generative models, and uncertainty
estimation methods in machine learning.
