Using a resource classifier in traffic forecasting models for SDN networks

Authors

DOI:

https://doi.org/10.18372/2073-4751.78.18960

Keywords:

traffic forecasting, network resource classifier, SDN, neural network

Abstract

This publication reviews existing solutions for predicting SDN traffic. The proposed solution uses a resource classifier of client requests for predicting the network response to these requests. This short-term forecasting is performed using a convolutional neural network. The article considers aspects of parallel computing and forecasting in a dynamically reconfigurable system. Forecasting results can be used in QoS/QoE services, SDN controller for traffic routing and detection of anomalous traffic by network protection systems.

References

Joshi M., Hadi T. H. A review of network traffic analysis and prediction techniques. arXiv:1507.05722. 2015. 22 p.

Nyaramneni S., Saifulla M. A., Shareef S. M. ARIMA for traffic load prediction in software defined networks. Lecture Notes on Data Engineering and Communications Technologies. Vol. 53. Evolutionary Computing and Mobile Sustainable Networks. Proceedings of ICECMSN 2020 / ed. by V. Suma, N. Bouhmala, H. Wang. Singapore, 2021. P. 815–824.

Yu B., Yang G., Yoo C. Comprehensive prediction models of control traffic for SDN controllers. 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft) : proceedings, Montreal, QC, Canada, 25–29 June 2018 / IEEE. 2018. P. 262–266.

Lim B., Zohren S. Time-series forecasting with deep learning: a survey. Philosophical Transactions of the Royal Society A. 2021. Vol. 379, no. 2194. 20200209.

Azzouni A., Pujolle G. NeuTM: A neural network-based framework for traffic matrix prediction in SDN. NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium : proceedings, Taipei, Taiwan, 23–27 April 2018 / IEEE. 2018. P. 1–5.

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Published

2024-07-01

How to Cite

Kulakov, Y., Korochkin, O., & Omelyanskiy, A. (2024). Using a resource classifier in traffic forecasting models for SDN networks. Problems of Informatization and Control, 2(78), 44–51. https://doi.org/10.18372/2073-4751.78.18960

Issue

Vol. 2 No. 78 (2024)

Section

Статті

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