sibsutisВестник СибГУТИThe Herald of the Siberian State University of Telecommunications and Information Science1998-6920СибГУТИ10.55648/1998-6920-2021-15-1-03-22sibsutis-40Research ArticleСтатьиАнализ перспективных подходов и исследований по классификации потоков трафика для поддержания QoS методами ML в SDN-сетяхAnalysis of promising approaches and research on traffic flow classification for maintaining QoS by ML methods in SDN networksДеартВ. Ю.DeartV. Yu.vdeart@mail.ruМаньковВ. А.MankovV. A.vladimir.mankov@gmail.comКрасноваИ. А.KrasnovaI. A.irina_krasnova-angel@mail.ruМТУСИRussian Federationучебный центр НокиаRussian Federation20211803202101323Copyright © Деарт В.Ю., Маньков В.А., Краснова И.А., 20212021Деарт В.Ю., Маньков В.А., Краснова И.А.Deart V.Y., Mankov V.A., Krasnova I.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.sibsutis.ru/jour/article/view/40

Одной из важнейших задач, существующих в современных сетях, является поддержка качества обслуживания QoS на соответствующем уровне, что может быть достигнуто путем применения различных механизмов управления трафиком. Но для того, чтобы поддерживать параметры QoS в надлежащем состоянии, требуется знать типы трафика, проходящие по сети. С учетом высокотехнологичных и производительных сетей, таких как SDN-сети, классификация трафика обычными способами становится практически невозможной. На помощь приходят методы интеллектуального анализа данных, в т.ч. методы машинного обучения. В статье анализируются основные перспективные подходы к классификации трафика в режиме реального времени для поддержания QoS в SDN-сетях методами ML, а также представлен сравнительный обзор наиболее выдающихся работ.

One of the most important tasks that exist in modern networks is to maintain the Quality-of-Service QoS at the appropriate level which can be achieved by applying various traffic management mechanisms. In order to maintain the QoS parameters in the proper state, you need to know the types of traffic passing through the network. Given high-tech and high-performance networks such as SDN networks, traffic classification by conventional methods becomes almost impossible. Data mining methods, including Machine Learning methods, come to the rescue. The article analyzes the main promising approaches to real-time traffic classification for maintaining QoS in SDN networks by ML methods as well as provides a comparative overview of the most outstanding works in this field.

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The authors declare that there are no conflicts of interest present.