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.

翻译：系统的高精度建模是工业数据分析的主要领域之一。系统的数字孪生模型用于预测其在不同条件下的行为。我们利用基于机器学习的生成模型开发了多个存储系统模型。该系统由多个组件组成：采用不同RAID方案和缓存的硬盘驱动器（HDD）与固态驱动器（SSD）存储池。每个存储组件通过概率模型表示，该模型描述了组件性能（以IOPS和延迟衡量）的概率分布，其取决于组件的配置和外部数据负载参数。实验结果表明，根据系统组件和模型的不同，IOPS预测误差为4-10%，延迟预测误差为3-16%。预测结果与利特尔法则的皮尔逊相关系数高达0.99，这可应用于模型的无监督可靠性检查。此外，我们提出了可用于机器学习中回归算法基准测试、条件生成模型评估以及不确定性估计方法验证的新型数据集。