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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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Только метаданные
ML-based Heterogeneous Container Orchestration Architecture
(2020) Rovnyagin, M. M.; Hrapov, A. S.; Guminskaia, A. V.; Orlov, A. P.; Ровнягин, Михаил Михайлович; Храпов, Александр Сергеевич
© 2020 IEEE.In recent years, the popularity of containerization technologies has been growing. When they are used, computational tasks are placed in lightweight containers that can be easily moved between different computing nodes. Containerization using Docker is especially popular at the moment. The use of these solutions opens up enormous opportunities for building distributed and cluster computing systems. To maintain the operability of such systems, special tools are used, and one of them is an orchestrator. However, existing orchestrators are focused on not-so-large computing systems in which performance can be maintained by simply moving computational tasks from non-working nodes to working ones. In large systems with many nodes and a huge number of computational tasks, it is also necessary to take into account the uneven consumption of resources by various tasks. This article proposes a system architecture that can solve the problem of container orchestration using machine learning methods and given the uneven consumption of resources by.
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Intelligent container orchestration techniques for batch and micro-batch processing and data transfer.
(2021) Rovnyagin, M. M.; Shipugin, V. A.; Ovchinnikov, K. A.; Durachenko, S. V.; Ровнягин, Михаил Михайлович
© 2020 Elsevier B.V.. All rights reserved.In the modern world, a large number of systems require the use of scaling, the provision standard for which at present time is orchestration and containerization technologies. In particular, information systems often operate with a data stream with different intensity, which requires the use of dynamic orchestration. This paper will propose methods for improving orchestration technology to increase the performance of data processing systems. The monograph proposes a method for optimizing the use of resources by predicting the load on the cluster. A method for efficient allocation of containers to physical nodes is described. A dynamic orchestration method based on system performance is also presented.
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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
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
Burrows - Wheeler Transform in lossless Data compression Problems on hybrid Computing Systems
(2020) Rovnyagin, M. M.; Varykhanov, S. S.; Sinelnikov, D. M.; Odintsev, V. V.; Ровнягин, Михаил Михайлович; Синельников, Дмитрий Михайлович
© 2020 IEEE.Currently, hybrid computing systems and clusters based on them are used to solve an increasing number of various tasks. This article addresses the issue of lossless data compression on hybrid computing systems. There are sections describing the development and implementation of a stack of lossless data compression algorithms based on the Burrows - Wheeler transform (BWT), as well as a section on data sorting on hybrid computing systems as one of the BWT steps. At the end are the test results of the proposed algorithms.
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.
Только метаданные
Intelligent Docker Container Orchestration for Low Scheduling Latency and Fast Migration in Paas
(2023) Rovnyagin, M. M.; Sinelnikov, D. M.; Varykhanov, S. S.; Magazov, T. R.; Kiamov, A. A.; Shirokikh, T. A.; Ровнягин, Михаил Михайлович; Синельников, Дмитрий Михайлович; Ровнягина, Татьяна Александровна
Только метаданные
Caching and Storage Optimizations for Big Data Streaming Systems
(2020) Rovnyagin, M. M.; Kozlov, V. K.; Mitenkov, R. A.; Gukov, A. D.; Yakovlev, A. A.; Ровнягин, Михаил Михайлович
© 2020 IEEE.Data processing is one of the most important processes in Big Data systems. In this paper, we propose a method and its performance model for data deduplication in distributed event driven software systems using Kafka streams and Apache Ignite cache, which reduces network and memory consumption. Also in this article the way of data storage systems optimization is considered by example of Apache Cassandra. The experiments showed that choosing of compression algorithms for different kinds of data with usage of neural network can help to find the balance between memory usage and read speed from the database.
Только метаданные
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
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.