Affiliation:
1. The Bonch-Bruevich Saint-Petersburg State University of Telecommunications
Abstract
With the advent of the concept of Software-Defined Networks with a centralized control plane, it became possible to collect a huge amount of information about the network, for example,
network topology, network congestion, the state of network devices. This data can be used to train machine learning algorithms. Moreover, these algorithms can be applied for completely different purposes, such as traffic classification, quality of service, optimization of traffic transmission routes, resource management, and security. Research subject of this article consist on the different aspects of SDN operating that can be optimized with ML. Research method is an analysis of the literature on the subject. Core results are analysis and classification of the areas and methods of the ML algorithms implementation for the SDN. Practical relevance of the work is that the results can be used for optimization of different SDN characteristics.
Publisher
Bonch-Bruevich State University of Telecommunications
Reference20 articles.
1. D. Clark, C. Partridge, J. Ramming, J. Wroclawski, “A Knowledge Plane for the In-ternet,” Proc SIGCOMM'03. 3-10, 2003.
2. G. Xu, Y. Mu, and J. Liu, “Inclusion of artifificial intelligence in communication networks and services,” ITU Journal: ICT Discoveries, no. 1, pp. 1–6, Oct. 2017.
3. P. Amaral, J. Dinis, P. Pinto, L. Bernardo, J. Tavares, and H. S. Mamede, “Machine learning in software defifined networks: Data collection and traffific classifification,” in Proc. IEEE ICNP’16, Singapore, Nov. 2016, pp. 1–5.
4. Koucheryavy A., Borodin A., Muthanna A., Abdellah A. R., Volkov A. Artificial Intelligence for Telecommunication Networks // 10TH International conference on advanced infotelecommunications, ICAIT 2021. РР. 8–18(in Russia).
5. R. Hajlaoui, H. Guyennet, and T. Moulahi, “A survey on heuristic-based routing methods in vehicular ad-hoc network: Technical challenges and future trends,” IEEE Sensors Journal, vol. 16, no. 17, pp. 6782–6792, Sept. 2016.