AI to Enable Accurate Modelling of Data Storage System Performance
Researchers at the HSE Faculty of Computer Science have developed a new approach to modelling data storage systems based on generative machine learning models. This approach makes it possible to accurately predict the key performance characteristics of such systems under various conditions. Results have been published in the IEEE Access journal.
Data storage systems play an important role in today’s digital world, as they are responsible for the safety and prompt availability of vast amounts of information. These systems consist of many components, including controllers, HDD and SSD disks, as well as cache memory, which work together to ensure fast and efficient operation. To achieve optimal performance, it is essential to accurately predict how these systems will function in different scenarios, such as when the load on the system changes.
Researchers at the HSE Faculty of Computer Science developed a new approach to modelling data storage system performance, which relies on generative machine learning models. The authors proposed a method that provides high-precision predictions of the key performance characteristics of the systems: the number of input/output operations per second (IOPS) and latency.
The modelling includes two stages. First, the scientists collect data by measuring the system’s performance under various loads and configurations. This data is then fed to two special generative models: the CatBoost regression model and the normalizing flow model. CatBoost works well with tabular data and can accurately predict average values and performance deviations. The normalizing flow model produces a complete distribution of possible outcomes, taking into account data uncertainties and variability.
Mikhail Hushchyn
‘One of the main advantages of our method is that it does not require detailed knowledge of the internal structure of the system components. This is often impossible due to the manufacturers’ trade secrets. Instead, our generative models are trained directly on real-world data. For instance, in our study, we trained a model using 300,000 measurements. This makes our approach versatile and applicable to any type of data storage system,’ says study author Mikhail Hushchyn, a senior research fellow at the HSE Faculty of Computer Science.
The researchers tested the accuracy of the proposed approach using Little's law, a fundamental principle of queuing theory. According to test results, these predictions are highly consistent with real observations: prediction errors range from just 4–10% for IOPS and 3–16% for latency, while the correlation with the observed values reaches 0.99.
Aziz Temirkhanov
‘Our proposed approach opens up broad prospects for optimising and planning the operation of data centres. It makes it possible to predict the behaviour of the system amid load changes, identify potential performance issues, and optimise power consumption. Furthermore, expensive physical experiments are no longer required for accurate modelling,’ stated Aziz Temirkhanov, a junior research fellow at the Laboratory of Methods for Big Data Analysis.
The experimental code and measurements of the storage system performance are publicly available.
The research was carried out within the Mirror Laboratories project of HSE University on improving the efficiency of data centres and data storage systems using artificial intelligence methods.
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