Abstract
The transmitting time of a packet between two devices is an essential factor in evaluating the network quality. Previous studies have applied machine learning to predict the connection delay value between two devices in traditional networks. However, there is little research using Software-Defined Networks (SDNs) because of the lack of SDNs traffic datasets. A method for collecting SDNs traffic data with delay values is proposed in this manuscript. In addition, this paper also evaluates different learning algorithms for SDNs delay prediction using the collected data. Experimental results showed that the Bidirectional LSTM had the lowest losses and the Recurrent Neural Networks had the shortest inference time.
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References
H. Feng, Y. Shu, Study on network traffic prediction techniques, in Proceedings of 2005 International Conference on Wireless Communications, Networking and Mobile Computing, 2005 (2005), pp. 1041–1044. https://doi.org/10.1109/WCNM.2005.1544219
H. Zare Moayedi, M.A. Masnadi-Shirazi, Arima model for network traffic prediction and anomaly detection, in 2008 International Symposium on Information Technology (2008), pp. 1–6. https://doi.org/10.1109/ITSIM.2008.4631947
R. Vinayakumar, K.P. Soman, P. Poornachandran, Applying deep learning approaches for network traffic prediction, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2017), pp. 2353–2358. https://doi.org/10.1109/ICACCI.2017.8126198
R. Madan, P.S. Mangipudi, Predicting computer network traffic: a time series forecasting approach using DWT, ARIMA and RNN, in 2018 Eleventh International Conference on Contemporary Computing (IC3) (2018), pp. 1–5. https://doi.org/10.1109/IC3.2018.8530608
N. Ramakrishnan, T. Soni, Network traffic prediction using recurrent neural networks, in 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (2018), pp. 187–193. https://doi.org/10.1109/ICMLA.2018.00035
W. Shihao, Z. Qinzheng, Y. Han, L. Qianmu, Q. Yong, A network traffic prediction method based on LSTM. ZTE Commun. 17(2), 19–25 (2019)
M.F. Iqbal, M. Zahid, D. Habib, L.K. John, Efficient prediction of network traffic for real-time applications. J. Comput. Netw. Commun. 2019 (2019). https://doi.org/10.1155/2019/4067135
T.P. Oliveira, J.S. Barbar, A.S. Soares, Computer network traffic prediction: a comparison between traditional and deep learning neural networks. Int. J. Big Data Intell 3, 28–37 (2016)
M.M. Raikar, M.S. Meena, M.M. Mulla, N.S. Shetti, M. Karanandi, Data traffic classification in software defined networks (SDNs) using supervised-learning. Proc. Comput. Sci. 171, 2750–2759 (2020). https://doi.org/10.1016/j.procs.2020.04.299
https://opennetworking.org/sdn-definition/ [Accessed 29/07/2021]
A. Yassine, H. Rahimi, S. Shirmohammadi, Software defined network traffic measurement: current trends and challenges software defined network traffic measurement: current trends and challenges. IEEE Instrum. Meas. Mag. 18(2), 42–50 (2015). https://doi.org/10.1109/MIM.2015.7066685
A.R. Mohammed, S.A. Mohammed, S. Shirmohammadi, Machine learning and deep learning based traffic classification and prediction in software defined networking. IEEE Int. Symp. Meas. Netw. (M &N) 2019, 1–6 (2019). https://doi.org/10.1109/IWMN.2019.8805044
M.A. Golberg, A.C. Hokwon, Introduction to Regression Analysis (WIT press, 2004)
A. Sherstinsky, Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Phys. D: Nonlinear Phenom. 404 (2020). https://doi.org/10.1016/j.physd.2019.132306
https://github.com/mininet/mininet/wiki/Documentation [Accessed 29/07/2021]
https://tcpreplay.appneta.com/wiki/howto.html [Accessed 29/07/2021]
https://opennetworking.org/onos/ [Accessed 29/07/2021]
S. Knight, H.X. Nguyen, N. Falkner, R. Bowden, M. Roughan, The internet topology zoo. IEEE J. Sel. Areas Commun. 29(9), 1765–1775 (2011). https://doi.org/10.1109/JSAC.2011.111002
https://www.wireshark.org [Accessed 29/07/2021]
http://bittwist.sourceforge.net/doc.html [Accessed 29/07/2021]
https://numpy.org/doc/stable/user/index.html [Accessed 29/07/2021]
https://pandas.pydata.org/docs/user_guide/index.html [Accessed 29/07/2021]
https://scikit-learn.org/stable/user_guide.html [Accessed 29/07/2021]
https://docs.scipy.org/doc/scipy/reference/ [Accessed 29/07/2021]
https://keras.io/guides/developer-guides [Accessed 29/07/2021]
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Dang, T.L., Ngo, N.M. (2023). SDNs Delay Prediction Using Machine Learning Algorithms. In: Phuong, N.H., Kreinovich, V. (eds) Biomedical and Other Applications of Soft Computing. Studies in Computational Intelligence, vol 1045. Springer, Cham. https://doi.org/10.1007/978-3-031-08580-2_13
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DOI: https://doi.org/10.1007/978-3-031-08580-2_13
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