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Ровнягин, Михаил Михайлович

Организационные подразделения

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Институт интеллектуальных кибернетических систем

Цель ИИКС и стратегия развития - это подготовка кадров, способных противостоять современным угрозам и вызовам, обладающих знаниями и компетенциями в области кибернетики, информационной и финансовой безопасности для решения задач разработки базового программного обеспечения, повышения защищенности критически важных информационных систем и противодействия отмыванию денег, полученных преступным путем, и финансированию терроризма.

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Ровнягин

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Михаил Михайлович

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Deep learning approach for QRS wave detection in ECG monitoring
(2019) Mitrokhin, M.; Kuzmin, A.; Mitrokhina, N.; Zakharov, S.; Rovnyagin, M.; Ровнягин, Михаил Михайлович
© 2017 IEEE. Paper describes an approach of deep learning for QRS wave detection for using in mobile heart monitoring systems. Authors analyze a deep learning approach and its advantages in the field of feature extraction and detection, and deep network architecture. Two different variants of deep network are proposed. ECG data processing scheme that includes a neural network is described. It presumes preprocessing, filtering, windowing of ECG signal, buffering, QRS wave detection and analysis. Network training process is mathematically founded. Two variants of neural network are experimentally tested. Training sets and test sets are obtained from free ECG data bank PhysioN et.org. Experimental results show that network with decreasing number of neurons in hidden layers has a better generalization capability. Next steps of research will include experiments with training set size and determining of its' influence on the quality of detection.
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Modeling NoSQL systems in many-nodes hybrid environments
(2019) Kuzmin, A. V.; Rovnyagin, M. M.; Chernilin, F. N.; Guminskaia, A. V.; Kinash, V. M.; Myltsyn, O. V.; Orlov, A. P.; Ровнягин, Михаил Михайлович
© 2017 IEEE. Data search is one of the most important problems in the field of computer science and computer facilities. Classical relational DBMSs (RDBMSs), unfortunately, are not suitable as data storage systems for Big Data. Therefore, the concept NoSQL is now widely spread. A common feature of such systems is a high throughput and linear scalability, depending on the number of storage servers used. One of the most productive NoSQL-systems, at the moment is Apache Cassandra. In this paper, we suggest ways to simulate the performance of such systems in hybrid computing environments.
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Algorithm of ML-based Re-scheduler for Container Orchestration System
(2021) Rovnyagin, M. M.; Dmitriev, S. O.; Hrapov, A. S.; Kozlov, V. K.; Ровнягин, Михаил Михайлович; Дмитриев, Святослав Олегович; Храпов, Александр Сергеевич
© 2021 IEEE.Due to the gradual growth of the number of companies that use cloud technologies, there is an increase in the number of enterprises deploying and using an internal private cloud. Due to this trend, there is growth of interest in various technologies that ensure the efficiency of the cloud infrastructure. One of such technologies is the orchestration technology, the core of which is a scheduler-a special component that allows efficiently distribute virtualized entities with running tasks across computational nodes. However, schedulers usually only plan the locations schemes of tasks that was not started yet; often they do not plan to make changes to the arrangement of already running entities. To create the plan of changing the state of already running tasks deschedulers and reschedulers are additionally used. This article proposes a solution using a Reinforcement Learning based rescheduler and an algorithm of its preparation.
Только метаданные
Database Storage Format for High Performance Analytics of Immutable Data
(2021) Rovnyagin, M. M.; Dmitriev, S. O.; Hrapov, A. S.; Maksutov, A. A.; Turovskiy, I. A.; Ровнягин, Михаил Михайлович; Дмитриев, Святослав Олегович; Храпов, Александр Сергеевич; Максутов, Артем Артурович
© 2021 IEEE.Most of modern database management systems offer a set of data manipulation operations, which strictly limits the available methods of data storage optimization. This article describes a database storage format that provides a low latency access to stored data with highly optimized sequential data extraction process by prohibiting any data modification after initially loading the data. The current study is aimed at developing a database management system that is suitable for high performance analytics of immutable data and performs better than database management systems with wider applicability. This paper includes developed data storage formats, data load and extraction algorithms and performance measurements.
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Methods for Speeding Up the Retraining of Neural Networks
(2022) Varykhanov, S. S.; Sinelnikov, D. M.; Odintsev, V. V.; Rovnyagin, M. M.; Mingazhitdinova, E. F.; Синельников, Дмитрий Михайлович; Ровнягин, Михаил Михайлович
© 2022 IEEE.Nowadays, machine learning is widespread and is becoming more complex. Developing and debugging neural networks is becoming more and more time-consuming. Distributed solutions are often used to speed up the learning process. But these solutions do not solve the problem of retraining model from zero if the learning fails. This paper presents a new approach to training models on a large datasets, which can save time and resources during the development. This approach is splitting the model's learning process into separate layers. Each of these layers can be modified and reused for the next layers. The implementation of this approach is based on transfer learning and distributed machine learning techniques. To create reusable network layers, it is proposed to use the methods of automating code parallelization for hybrid computing systems described in the article. These methods include: tracking the readiness and dependencies in the data, speculative execution at the kernel level, creating a DSL
Только метаданные
Approach of Program's Concurrency Evaluation in PaaS Cloud Infrastructure
(2022) Mingazhitdinova, E. F.; Sinelnikov, D. M.; Odintsev, V. V.; Rovnyagin, M. M.; Varykhanov, S. S.; Синельников, Дмитрий Михайлович; Ровнягин, Михаил Михайлович
© 2022 IEEE.In the most cases programs have got different level of concurrency. According to Amdahl's Law, changing the amount of resources may not give a gain in computational efficiency. The article's goal which was determined by the authors is to find out the balance between increasing the amount of resources and improving the efficiency that can be obtained for computation in the K8s cluster. In an effort of resolving the task authors have decided to explore the correlation between increasing the number of podes and time of Spark program processing (data-intensive and compute-intensive computations) in the local minikube for following deployment in K8s.
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Методы и средства решения задач поиска и защищенного хранения данных с применением гибридных вычислительных технологий
(НИЯУ МИФИ, 2015) Ровнягин, М. М.; Ровнягин, Михаил Михайлович; Васильев, Н. П.
Только метаданные
Cloud computing architecture for high-volume ML-based solutions
(2019) Rovnyagin, M. M.; Kirill, Timofeev, V.; Elenkin, A. A.; Shipugin, V. А.; Ровнягин, Михаил Михайлович
© 2019 IEEE A large number of modern projects use machine learning technology to perform a variety of business calculations. There are two main ways to integrate machine-learning models into the logic of industrial applications. The first way is to rewrite models from the data analysis language (for example R or Python) to the industrial development language (for example Java, Go or Scala). The second way is to equip models with a web-interface and integrate it into the calculation. In this article, we explore the second method. A deployment architecture for machine learning in the clouds is proposed. The possibilities of the proposed scheme for scaling are described. Examples of practical use of the proposed architecture for organizing data storage with compression are also given.
Только метаданные
Orchestration of CPU and GPU Consumers for High-Performance Streaming Processing
(2021) Rovnyagin, M. M.; Gukov, A. D.; Timofeev, K. V.; Hrapov, A. S.; Mitenkov, R. A.; Ровнягин, Михаил Михайлович; Храпов, Александр Сергеевич
© 2021 IEEE.In the modern world, there are many systems using streaming data processing. Often, these systems use CPU and GPU devices in their calculations. It should be noted that such systems can fail for various reasons. Therefore, to optimize throughput, system designers need to determine in advance how many CPUs and GPUs to configure the system with. In our article, we present a possible architecture of such a system and present what methods can be used to calculate the optimal number of CPUs and GPUs with optimal throughput and taking into account other factors, for example, the cost of devices and the failure rate of the environment.
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Data Exchange Acceleration Methods in a Decentralized File System
(2023) Rovnyagin, M. M.; Sinelnikov, D. M.; Varykhanov, S. S.; Khudoyarova, A. M.; Yakovenko, I. A.; Shirokikh, T. A.; Ровнягин, Михаил Михайлович; Синельников, Дмитрий Михайлович; Яковенко, Иван Алексеевич; Ровнягина, Татьяна Александровна