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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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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.
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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Using the machine learning methods for resource management of high availability broadcasting containerized system
(2020) Aminova, A.; Orlov, A.; Rovnyagin, M.; Guminskaia, A.; Chernilin, F.; Hrapov, A.; Ровнягин, Михаил Михайлович; Храпов, Александр Сергеевич
© 2020 The Authors. Published by Elsevier B.V.Most of today applications are built on a micro-service architecture, where a large application is divided into different functional parts that can be deployed on many containers that enable good load balancing. Container management tools need system load forecasting means to timely balance system load. It is an important problem for systems with direct streams of popular events which periodically have large splashes of a load. In this paper, we propose the load prediction method for such systems in two cases: Usual and broadcasting workload. Also, we propose an architecture of adaptive infrastructure using our load forecasting method.
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
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
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.
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
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
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
Presentation of the PaaS System State for Planning Containers Deployment Based on ML-Algorithms
(2020) Rovnyagin, M. M.; Hrapov, A. S.; Ровнягин, Михаил Михайлович; Храпов, Александр Сергеевич
© 2020 IEEE.In modern world one of the most important technologies is virtualization. And one of the most promising types of virtualization is OS-level virtualization, also known as containerization. Its use greatly simplifies the task of deploying stable computing system services that are performed on suitable hardware depending on the current situation.Various additional tools are used to automating the process of managing the location of the containers.However, most existing container management tools provide only the simplest behaviors. One of the more complex tasks that cannot be solved by such tools can be represented as follows: There are several virtualized entities (containers) that can be executed on cluster nodes. Each entity contains a task that consumes a certain amount of computing resources. It is necessary to distribute entities among nodes in such a way that each of them has enough resources.This paper proposes a more complex methodology that solves the proposed problem of service management using machine learning methods.