Analyzing Traffic Identification Methods for Resource Management in SDN
https://doi.org/10.31854/1813-324X-2023-9-6-42-57
Abstract
The article is devoted to the analysis of traffic classification methods in SDN network. The review of analytical approaches of traffic identification to identify the solutions used in them, as well as assessing their applicability in the SDN network. Types of machine learning are considered and input parameters are analyzed. The methods of intelligent analysis covered in the scientific articles are systematized according to the following criteria: traffic identification parameters, neural network model, identification accuracy. Based on the analysis of the review results, the conclusion is made about the possibility of applying the considered solutions, as well as the need to form a scheme of SDN network with a module of artificial intelligence elements for load balancing.
Keywords
Software-Defined Networking (SDN),
Deep packet inspection (DPI),
Machine learning (ML),
Deep learning (DL),
Convolutional Neural Network (CNN),
Artificial Intelligence (AI)
Supporting agencies: The scientific article was prepared in the framework of applied scientific research of SPBSUT, registration number 123060900012-6 in EGISU R&D.
About the Authors
J. Dmitrieva
The Bonch-Bruevich Saint-Petersburg State University of Telecommunications
Russian Federation
Russian Federation
D. Okuneva
The Bonch-Bruevich Saint-Petersburg State University of Telecommunications
Russian Federation
Russian Federation
V. Elagin
The Bonch-Bruevich Saint-Petersburg State University of Telecommunications
Russian Federation
Russian Federation
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Dmitrieva J., Okuneva D., Elagin V. Analyzing Traffic Identification Methods for Resource Management in SDN. Proceedings of Telecommunication Universities. 2023;9(6):42-57. (In Russ.) https://doi.org/10.31854/1813-324X-2023-9-6-42-57
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