Intelligent Congestion Control of 5G Traffic in SDN using Dual-Spike Neural Network
Keywords:5G Communications, Software Defined Networking, Virtualized Network Function, Intelligent spike neural network
Software Defined Networking (SDN) with centralized control provides a global view and achieves efficient network resources management. However, using centralized controllers has several limitations related to scalability and performance, especially with the exponential growth of 5G communication. This paper proposes a novel traffic scheduling algorithm to avoid congestion in the control plane. The Packet-In messages received from different 5G devices are classified into two classes: critical and non-critical 5G communication by adopting Dual-Spike Neural Networks (DSNN) classifier and implementing it on a Virtualized Network Function (VNF). Dual spikes identify each class to increase the reliability of the classification. Different metrics have been adopted to evaluate the proposed classifier's effectiveness: accuracy, precision, recall, Matthews Correlation Coefficient (MCC), and F1-Score. Compared with a convolutional neural network (CNN), the simulation results confirmed that the DSNN model could enhance traffic classification accuracy by 5%. The efficiency of the priority model also has been demonstrated in terms of Round Trip Time (RTT).
Article received: 26/7/2022
Article accepted: 11/9/2022
Article published: 1/1/2023
Al-Jamali, N. A. S., 2020. Convolutional Multi-Spike Neural Network as Intelligent System Prediction for Control Systems, Journal of Engineering, vol. 26, no. 11, pp. 184–194.
Hu, N., Luan, F., Tian, X., and Wu, C., 2020. A novel sdn-based application wareness mechanism by using deep learning, IEEE Access, vol. 8, pp. 160 921–160 930
Kamath, S., Singh, S., and Kumar, M.S., 2020. Multiclass Queuing Network Modeling and Traffic Flow Analysis for SDN-Enabled Mobile Core Networks with Network Slicing, IEEE Access, vol. 8, 8932489, pp. 417-430, https://doi.org/10.1109/ACCESS.2019.2959351.
Khan, S., Khan, S., Ali, Y., Khalid, M., Ullah, Z., and Mumtaz, S., 2022. Highly
accurate and reliable wireless network slicing in 5th generation networks:
A hybrid deep learning approach, Journal of Network and Systems
Management, vol. 30, no. 2, pp. 1–22.
Liu, T., Liu, Z., Lin, F., Jin, Y., Quan, G., and Wen, W., 2017. Mt-spike: A multilayer time-based spiking neuromorphic architecture with temporal error back propagation, IEEE/ACM International Conference on Computer-Aided Design (ICCAD). IEEE, pp. 450–457.
Malik, A., de Fréin, R., Al-Zeyadi, M., and Andreu-Perez, J., 2020. Intelligent sdn traffic classification using deep learning: Deep-sdn, 2nd International Conference on Computer Communication and the Internet (ICCCI). IEEE, pp. 184–189.
Miao, Y., Tang, H. and Pan, G., 2018. A Supervised Multi-Spike Learning Algorithm for Spiking Neural Networks, Proceedings of the International Joint Conference on Neural Networks. IEEE, July, DOI: 10.1109/IJCNN.2018.8489175.
Mondal, S., and Misra, S., 2020. FlowMan: QoS-Aware Dynamic Data Flow Management in Software-Defined Networks, IEEE Journal on Selected Areas in Communications, vol. 38, no. 7, pp. 1366-1373, DOI: 10.1109/JSAC.2020.2999682.
Morocho-Cayamcela, M. E., Lee, H., and Lim, W., 2019. Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions, IEEE Access, 7. 137184-137206. 10.1109/ACCESS.2019.2942390.
Owusu, I. and Nayak, A., 2020 An Intelligent Traffic Classification in SDN-IoT: A Machine Learning Approach, IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), pp. 1-6, DOI: 10.1109/BlackSeaCom48709.2020.9235019.
Preciado-Velasco, J.E., Gonzalez-Franco, J.D., Anias-Calderon, C.E., Nieto-Hipolito, J.I., and Rivera-Rodriguez, R., 2021. 5G/B5G Service Classification Using Supervised Learning, Applied Sciences, 11, 4942, https://doi.org/10.3390/app11114942.
Raikar, M. M., M, M. S., Mulla, M. M., Shetti, N. S., Karanandi, M., 2020. Data Traffic Classification in Software Defined Networks (SDN) using supervised-learning, Procedia Computer Science, Volume 171, pp. 2750-2759.
Shi, C., Wang, T., He, J., Zhang, J., Liu, L., and Wu, N., 2021. Deeptempo: a hardware-friendly direct feedback alignment multi-layer tempotron learning rule for deep spiking neural networks, IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 5, pp. 1581–1585.
Shiltagh, N. A. and Abas, H. A., 2014. Spiking Neural Network in Precision Agriculture, Journal of Engineering, 3(7), p. 17-34.
Soud, N. S., Al-Jamali, N. A. S., and Al-Raweshidy, H. S., 2022. Moderately Multispike Return Neural Network for SDN Accurate Traffic Awareness in Effective 5G Network Slicing, IEEE Access, vol. 10, no. June, pp. 73378–73387, doi:10.1109/ACCESS.2022.3189354.
Taherkhani, A., Belatreche, A., Li, Y., Cosma, G., Maguire, L. P., and McGinnity, T. M., 2020. A review of learning in biologically plausible spiking neural networks. Neural networks, The Official Journal of the International Neural Network Society, 122, 253–272. https://doi.org/10.1016/j.neunet.2019.09.036.
Thantharate, A., Paropkari, R., V. Walunj, and Beard, C., 2019. Deepslice: A deep learning approach towards an efficient and reliable network slicing in 5g networks, IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 0762–0767.
Xu, Y., Zeng, X., Han, L., and Yang, J., 2013 A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks, Neural Networks, vol. 43, pp. 99–113.
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