Traffic Prediction-Based Routing in Software-Defined Networking Using Long Short-Term Memory and Harmony Search

Subject Areas : Computer Engineering

Khadijeh Mirzaei Talarposhti ¹ , Sam Jabbehdari ² , Amir Masoud Rahmani ³

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Received: 2025-09-23 Accepted : 2025-11-02 Published : 2025-12-09

Keywords: Software Defined Networking, Long Short-Term Memory, Harmony Search, Intelligent Routing,

Abstract :

Efficient traffic management in Software-Defined Networking (SDN) relies heavily on accurate traffic prediction to enable intelligent routing decisions. In this paper, we propose a novel routing framework that integrates deep learning-based traffic forecasting with an optimization-driven path selection mechanism. Specifically, Long Short-Term Memory (LSTM) networks are employed to predict future traffic patterns based on time-series data extracted from SDN switches. To enhance prediction accuracy, Fast Fourier Transform (FFT) is used to enrich the input features with dominant traffic harmonics. Furthermore, key forecasting parameters such as the number of lags, labels, and shifts are optimized using the Harmony Search (HS) algorithm. The predicted traffic volumes are then utilized to dynamically adjust routing paths, aiming to minimize latency, jitter, and packet loss while improving throughput. The proposed framework is implemented on a Mininet-based SDN testbed using a customized Ryu controller. Experimental results under both regular and chaotic traffic scenarios demonstrate that the LSTM-based forecasting significantly improves routing efficiency compared to traditional approaches, especially when enhanced by FFT preprocessing and HS optimization. This study highlights the potential of combining predictive intelligence and metaheuristic tuning to achieve adaptive and robust traffic-aware routing in SDN environments.

References:

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[4] M. Al-Fares, S. Radhakrishnan, B. Raghavan, N. Huang, A. Vahdat, Hedera: dynamic flow scheduling for data center networks, Nsdi, San Jose, USA, 2010, pp. 89-92
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[6] F. Francois, E. Gelenbe, Towards a cognitive routing engine for software defined networks, 2016 IEEE International Conference on Communications (ICC), 2016, pp. 1-6.10.1109/ICC.2016.7511138
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[8] G. Wassie Geremew, J. Ding, Elephant Flows Detection Using Deep Neural Network, Convolutional Neural Network, Long Short-Term Memory, and Autoencoder, Journal of Computer Networks and Communications 2023(1) (2023) 1495642.https://doi.org/10.1155/2023/1495642
[9] P.K. Hoong, I.K. Tan, C.Y. Keong, Bittorrent network traffic forecasting with ARMA, arXiv preprint arXiv:1208.1896 (2012)
[10] N. Hoong, P. Hoong, I. Tan, N. Muthuvelu, L. Seng, Impact of utilizing forecasted network traffic for data transfers, 13th International Conference on Advanced Communication Technology (ICACT2011), IEEE, 2011, pp. 1199-1204
[11] Y. Yu, M. Song, Y. Fu, J. Song, Traffic prediction in 3G mobile networks based on multifractal exploration, Tsinghua Science and Technology 18(4) (2013) 398-405.10.1109/TST.2013.6574678
[12] B. Vujicic, C. Hao, L. Trajkovic, Prediction of traffic in a public safety network, 2006 IEEE International Symposium on Circuits and Systems (ISCAS), 2006, p. 4 pp.10.1109/ISCAS.2006.1693165
[13] N.C. Anand, C. Scoglio, B. Natarajan, GARCH non-linear time series model for traffic modeling and prediction, NOMS 2008 - 2008 IEEE Network Operations and Management Symposium, 2008, pp. 694-697.10.1109/NOMS.2008.4575191
[14] P. Cortez, M. Rio, M. Rocha, P. Sousa, Internet Traffic Forecasting using Neural Networks, The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006, pp. 2635-2642.10.1109/IJCNN.2006.247142
[15] S. Chabaa, A. Zeroual, J. Antari, Identification and prediction of internet traffic using artificial neural networks, Journal of Intelligent Learning Systems and Applications 2(3) (2010) 147-155
[16] D.C. Park, D.M. Woo, Prediction of Network Traffic Using Dynamic Bilinear Recurrent Neural Network, 2009 Fifth International Conference on Natural Computation, 2009, pp. 419-423.10.1109/ICNC.2009.662
[17] S. Chabaa, A. Zeroual, J. Antari, ANFIS method for forecasting internet traffic time series, 2009 Mediterrannean Microwave Symposium (MMS), 2009, pp. 1-4.10.1109/MMS.2009.5409834
[18] Y. Liang, L. Qiu, Network traffic prediction based on SVR improved by chaos theory and ant colony optimization, International journal of future generation communication and networking 8(1) (2015) 69-78
[19] P. Rai, H.K. Deva Sarma, A Survey on Application of LSTM as a Deep Learning Approach in Traffic Classification for SDN, in: H.K. Deva Sarma, V. Piuri, A.K. Pujari (Eds.) Machine Learning in Information and Communication Technology, Springer Nature Singapore, Singapore, 2023, pp. 161-173
[20] H. Yao, C. Liu, P. Zhang, S. Wu, C. Jiang, S. Yu, Identification of Encrypted Traffic Through Attention Mechanism Based Long Short Term Memory, IEEE Transactions on Big Data 8(1) (2022) 241-252.10.1109/TBDATA.2019.2940675
[21] K.M.R. Mokoena, R.L. Moila, P.M. Velempini, Improving Network Management with Software Defined Networking using OpenFlow Protocol, 2023 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), 2023, pp. 1-5.10.1109/icABCD59051.2023.10220519
[22] O.N. Fundation, Software-defined networking: The new norm for networks, ONF White Paper 2(2-6) (2012) 11
[23] N. McKeown, T. Anderson, H. Balakrishnan, G. Parulkar, L. Peterson, J. Rexford, S. Shenker, J. Turner, OpenFlow: enabling innovation in campus networks, SIGCOMM Comput. Commun. Rev. 38(2) (2008) 69–74.10.1145/1355734.1355746
[24] Z. Motazedian, A.A. Safavi, Nonlinear and Time Varying System Identification Using a Novel Adaptive Fully Connected Recurrent Wavelet Network, 2019 27th Iranian Conference on Electrical Engineering (ICEE), 2019, pp. 1181-1187.10.1109/IranianCEE.2019.8786669
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