OPTIMIZATION OF ROUTING USING TRAFFIC CLASSIFICATION IN SOFTWARE DEFINED NETWORKING
DOI:
https://doi.org/10.55766/sujst-2023-01-e01890Keywords:
Software Defined Networking, Quality of service, POX controller, QoS classesAbstract
Efficient routing is an essential task for any network as it directly impacts the network’s performance. In this paper, we have used traffic classification techniques to optimize routing in Software Defined Network (SDN) and provided a cost aware routing framework. Instead of classifying traffic based on a particular parameter, we perform the traffic classification using a hybrid approach that is based on machine learning techniques. Our framework uses supervised machine learning techniques to classify network flow and detect elephant flows which are too heavy in size and require significant bandwidth. As the requirements of each elephant flow vary with the application, we further perform Quality of Service (QoS) based traffic classification, which classifies these elephant flow into QoS classes as per their QoS requirements. For this purpose, we have used semi-supervised machine learning algorithms. Further, we have proposed a routing algorithm that makes use of the Dijkstra algorithm to compute the best possible shortest path based on the QoS requirements of the elephant flow. The proposed method was implemented and tested on Mininet using a POX controller. The simulation results show that our framework successfully classifies elephant flows with an accuracy of 80% and also computes a low-cost path for each elephant flow based on the actual internet data set.
References
Akella, A., and Maltz, D.A. (2010). Network Traffic characteristics of data centers in the wild. Proceedings of the 10th ACM SIGCOMM conference on Internet measurement, NY, United States., p. 267-280.
Azzouni, A., Boutaba, R., and Pujolle, G. (2017). Neuroute: Predictive dynamic routing for software-defined networks. 13th International Conference on Network and Service Management (CNSM), Tokyo, Japan, p. 1-6.
Dieng, A.B., and Paisley, J. (2019) . Reweighted expectation maximization. arXiv.org > stat > arXiv: 1906. 05850, 2019. Iprf, (2020).
Dijkstra, E.W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik., 1(1):269-271. doi:10.1007/BF01386390.
Hosmer, D. W., Lemeshow, S., and Sturdivant, R. X. (2013). Applied logistic regression (3rd ed.). John Wiley & Sons.
Lin, S., Akyildiz, I.F., Wang, P., and Luo, M. (2016). Qos-aware adaptive routing in multi-layer hierarchical software - defined networks: A reinforcement learning approach. IEEE International Conference on Services Computing (SCC), San Francisco, CA, USA., p. 25-33.
Malik, Ali., de Fréin, Ruairí., al-zeyadi, Mohammed., and Andreu-Perez, Javier. (2020). Intelligent SDN Traffic Classification using Deep Learning.
Marsan, M.A., and Donnet, B. (2003). A self-contained and efficient IP traffic generator. ACM SIGCOMM Computer Communication Review., 33(2):105-112. doi:10.1145/948305.948331
Open Networking Foundation [ONF], (2020). Software-defined Networking: The new norm for networks. Palo Alto, CA 94303 Available from: www.opennetworking.org, Accessed date: April 2020.
Pasca S.T.V., Kodali, S.S.P., and Kataoka, K. (2017). AMPS: Application-aware multipath flow routing using machine learning in sdn. in 2017 Twenty-third National Conference on Communications (NCC), Chennai, India., p. 1-6.
Patrushev, G.G., and Drozdova, V.G. (2017). Routing efficiency evaluation with SDN solutions integration in the data network. // 18th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices (EDM), Erlagol Altai., pp. 190-194.
Phemius, K., and Bouet, M. (2013) . Monitoring latency with openflow. 9th International conference on Network and service management (CNSM), Zurich, Switzerland., p. 122-125.
Wnag, P., Lin, S., and Luo, M. (2016). A framework for qosaware traffic classification using semi-supervised machine learning in sdn. in 2016 IEEE International Conference on Services Computing (SCC), San Francisco. CA., p. 760-765.
Xiao, P., Qu, W., Qi, H., Xu, Y., and Li, Z. (2015). An Efficient Elephant Flow Detection with Cost-Sensitive in SDN. In Proceedings of the 6th International Conference on Internet and Networked Systems (ICINS), doi: 10.4108/icst.iniscom.2015.258274.
Xie, J., Yu, F.R., Huang, T., Xie, R., Liu, J., Wang, C., and Liu, Y. (2019). A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges. in IEEE Communications Surveys and Tutorials., 21(1):393-430.
Yahyaoui, h., Aidi, S., and Zhani, M.F. (2020). On using flow classification to optimize traffic routing in sdn networks. IEEE 17th Annual Consumer Communications Networking Conference (CCNC), Las Vegas, USA., p. 1-6.
