A Traffic Classification and Prioritization Model in Software-Defined Networks
https://doi.org/10.31854/1813-324X-2019-5-1-64-70
Abstract
As the number of networked devices and applications rapidly grows, particularly the Internet of Things applications, billions of devices are connected to the network and therefore managing the generated traffic becomes a needy task. Effectively managing these devices to support reliable, secure, and high-quality applications becomes challenging. The main solution to manage network traffic is the automatic classification of application aimed at identifying the semantic type of application by analyzing its network traffic and wide range of new features. This article proposes a model for dynamic network traffic classification in software-defined networks based on the modified k-means algorithm for network resources distribution to prioritized types of traffic, which allows network applications optimization.
About the Authors
S. .. Muhizi
The Bonch-Bruevich Saint-Petersburg State University of Telecommunications
Russian Federation
Russian Federation
A. .. Paramonov
The Bonch-Bruevich Saint-Petersburg State University of Telecommunications
Russian Federation
Russian Federation
References
1. Кучерявый А.Е. Интернет Вещей // Электросвязь. 2013. № 1. С. 21-24.
2. Гольдштейн Б.С., Кучерявый А.Е. Сети связи пост-NGN. СПб: БХВ-Петербург, 2013. 160 с.
3. Бородин А.С., Кучерявый А.Е. Сети связи пятого поколения как основа цифровой экономики // Электросвязь. 2017. № 5. С. 45-49.
4. Muhizi S., Ateya A.A., Muthanna A., Kirichek R., Koucheryavy A. A Novel Slice-Oriented Network Model // Vishnevskiy V.M., Kozyrev D.V. Distributed Computer and Communication Networks. Communications in Computer and Information Science. Proceedings of the 21st International Conference (DCCN, Moscow, Russia, 17-21 September, 2018). Cham: Springer, 2018. Vol. 919. PP. 421-431. DOI:10.1007/978-3-319-99447-5_36
5. Мухизи С., Мутханна А.С., Киричек Р.В, Кучерявый А.Е. Исследование моделей балансировки нагрузки в программно-конфигурируемых сетях // Электросвязь. 2019. № 1. С. 23-29.
6. Vladyko A., Letenko I., Lezhepekov A., Buinevich M. Fuzzy Model of Dynamic Traffic Management in Software-Defined Mobile Networks // Galinina O., Balandin S., Koucheryavy Y. Internet of Things, Smart Spaces, and Next Generation Networks and Systems. Proceedings of the 16th International Conference, NEW2AN, and the 9th Conference, ruSMART (St. Petersburg, Russia, 26-28 September 2016). Lecture Notes in Computer Science. Cham: Springer, 2016. Vol. 9870. PP. 561-570. DOI:10.1007/978-3-319-46301-8_47
7. Гимадинов Р.Ф., Мутханна А.С., Кучерявый А.Е. Кластеризация в мобильных сетях 5G. Случай частичной мобильности // Информационные технологии и телекоммуникации. 2015. Т. 3. № 2. С. 44-52.
8. Muhizi S., Shamshin G., Muthanna A., Kirichek R., Vladyko A., Koucheryavy A. Analysis and Performance Evaluation of SDN Queue Model // Koucheryavy Y., Mamatas L., Matta I., Ometov A., Papadimitriou P. (eds.) Wired/Wireless Internet Communications. Proceedings of the 15th IFIP WG 6.2 International Conference (WWIC, St. Petersburg, Russia, 21-23 June 2017). Lecture Notes in Computer Science. Cham: Springer, 2017. Vol. 10372. PP. 37-48. DOI:10.1007/978-3-319-61382-6_3
9. Muthanna A., Volkov A., Khakimov A, Muhizi S., Kirichek R., Koucheryavy A. Framework of QoS Management for Time Constraint Services with Requested Network Parameters based on SDN/NFV Infrastructure // Proceedings of the 10th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT, Moscow, Russia, 5-9 November, 2018). Piscataway, NJ: IEEE, 2018. DOI:10.1109/ICUMT.2018.8631274
10. Rec. ITU-T Y.3110 (09/2017). IMT-2020 network management and orchestration requirements & framework.
11. Rec. ITU-T Y.3112 (05/2018). Framework for the support of Multiple Network Slicing.
12. Rec. ITU-T Y.3150 (01/2018). High-level technical characteristics of network softwarization for IMT-2020.
13. Kirichek R., Vladyko A., Paramonov A., Koucheryavy A. Software-defined architecture for flying ubiquitous sensor networking // Proceedings of the 19th International Conference on Advanced Communication Technology (ICACT, Bongpyeong, South Korea, 19-22 February 2017). Piscataway, NJ: IEEE, 2017. PP. 158-162. DOI:10.23919/ICACT.2017.7890076
14. Мухизи C., Киричек Р.В. Анализ технологии слайсинга в сетях связи пятого поколения // Информационные технологии и телекоммуникации. 2017. Т. 5. № 4. С. 57-63.
15. Ksentini A., Nikaein N. Toward Enforcing Network Slicing on RAN: Flexibility and Resources Abstraction // IEEE Communications Magazine. 2017. Vol. 55. Iss. 6. PP. 102-108. DOI:10.1109/MCOM.2017.1601119
16. Zander S., Armitage G. Practical machine learning based multimedia traffic classification for distributed QoS management // Proceedings of the 36th Annual IEEE Conference on Local Computer Networks (LCN, Bonn, Germany, 4-7 October 2011). Piscataway, NJ: IEEE, 2011. PP. 399-406. DOI:10.1109/LCN.2011.6115322
17. Mahdavinejad S.M., Rezvan M., Barekatain M., Adibi P., Barnaghi P., Sheth A.P. Machine learning for internet of things data analysis: a survey // Digital Communications and Networks. 2018. Vol. 4. Iss. 3. PP. 161-175. DOI:10.1016/j.dcan.2017.10.002
18. Bair E. Semi-supervised clustering methods // WIREs Computational Statistics. 2013. Vol. 5. Iss. 5. PP. 349-361. DOI:10.1002/wics.1270
19. Wireshark. URL: https://www.wireshark.org (дата обращения 22.03.2019)
Review
For citations:
Muhizi S..., Paramonov A... A Traffic Classification and Prioritization Model in Software-Defined Networks. Proceedings of Telecommunication Universities. 2019;5(1):64-70. (In Russ.) https://doi.org/10.31854/1813-324X-2019-5-1-64-70
Views:
465
Email this article 