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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
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.
Только метаданные
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.
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.
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
Optimizing Cache Memory Usage Methods for Chat LLM-models in PaaS Installations
(2024) Rovnyagin, M. M.; Sinelnikov, D. N.; Eroshev, A. A.; Rovnyagina, T. A.; Tikhomirov, A. V.; Ровнягин, Михаил Михайлович; Синельников, Дмитрий Николаевич; Ерошев, Артём Александрович; Тихомиров, Александр Владимирович; Ровнягина, Татьяна Александровна
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
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
Distributed Fault-tolerant Platform for Web Applications
(2020) Rovnyagin, M. M.; Sinelnikov, D. M.; Odintsev, V. V.; Varykhanov, S. S.; Ровнягин, Михаил Михайлович; Синельников, Дмитрий Михайлович
© 2020 IEEE.Web applications are software applications, services or microservices that runs on a remote server. The problem of downtime for web application is important and in some cases, is critical for business. Nowadays, cluster solutions are often used to provide fault-tolerance for applications. But these solutions don't solve the problem of downtime if all instances of application are down. This paper presents a complex approach to provide fault-tolerance for web applications even if all instances of applications in the cluster are down. The approach is based on long-polling and request queueing methods. In this work Apache Kafka and Google Protocol Buffers has been used as the core for the fault-tolerant platform.