Анализ перспективных подходов и исследований по классификации потоков трафика для поддержания QoS методами ML в SDN-сетях
Аннотация
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.
Об авторах
Владимир Юрьевич Деарт
МТУСИ
Россия
Россия
Владимир Александрович Маньков
учебный центр Нокиа
Россия
Россия
Ирина Артуровна Краснова
МТУСИ
Россия
Россия
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Рецензия
Для цитирования:
Деарт В.Ю., Маньков В.А., Краснова И.А. Анализ перспективных подходов и исследований по классификации потоков трафика для поддержания QoS методами ML в SDN-сетях. Вестник СибГУТИ. 2021;(1):3-23. https://doi.org/10.55648/1998-6920-2021-15-1-03-22
For citation:
Deart V.Yu., Mankov V.A., Krasnova I.A. Analysis of promising approaches and research on traffic flow classification for maintaining QoS by ML methods in SDN networks. The Herald of the Siberian State University of Telecommunications and Informatics. 2021;(1):3-23. (In Russ.) https://doi.org/10.55648/1998-6920-2021-15-1-03-22
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