Video Streaming Service Identification on Software-Defined Networking
Keywords:
livestream, KDN, SDN, Traffic Classification, VoDAbstract
It is well known that video streaming is the major network traffic today. Futhermore, the traffic generated by video streaming is expected to increase exponentially. On the other hand, SoftwareDefined Networking (SDN) has been considered a viable solution to cope with the complexity and increasing network traffic due to its centralised control and programmability features. These features, however, do not guarantee that network performance will not suffer as traffic grows. As result, understanding video traffic and optimising video traffic can aid in control various aspects of network performance, such as bandwidth utilisation, dynamic routing, and Quality of Service (QoS). This paper presents an approach to identify video streaming traffic in SDN and investigates the feasibility of using Knowledge-Defined Networking (KDN) in traffic classification. KDN is a networking paradigm that takes advantage of Artificial Intelligence (AI) by using Machine Learning approaches, which allows integrating behavioural models to detect patterns, like video streaming traffic identification, in SDN traffic. In our initial proof-of-concept, we derive the relevant information of network traffic in the form of flows statistics. Then, we used such information to train six ML models that can classify network traffic into three types, Video on Demand (VoD), Livestream, and no-video traffic. Our proof-of-concept demonstrates that our approach is applicable and that we can identify and classify video streaming traffic with 97.5% accuracy using the Decision Tree model.
References
[2] Bakhshi, T. (2017). Multi-feature Enterprise Traffic Characterization in OpenFlow-based Software Defined Networks, International Conference on Frontiers of Information Technology, 23-28, 2017. https://doi.org/10.1109/FIT.2017.00012
[3] Bishop, C. M.; Nasrabadi, N. (2007). Pattern Recognition and Machine Learning, J. Electronic Imaging, 16, 049-901, 2007.
[4] Boutaba, R.; Salahuddin, M. A.; Limam, N. et al (2019). A comprehensive survey on machine learning for networking: evolution, applications and research opportunities, Journal of Internet Services and Applications, 9(1), 1-99, 2010. https://doi.org/10.1186/s13174-018-0087-2
[5] Callado, A. et al (2009). A Survey on Internet Traffic Identification, IEEE Communications Surveys & Tutorials, 11(3), 37-52, 2009. https://doi.org/10.1109/SURV.2009.090304
[6] Cios, K. J.; Pedrycz, W.; Swiniarski, R. W.; Kurgan, L (2007). Data Mining: A Knowledge Discovery Approach, Springer-Verlag, 2007.
[7] Cisco, Cisco Visual Networking Index: Forecast and Trends, 2017-2022, pp. 147, 2019. [Online]. Available: https://davidellis.ca/wp-content/uploads/2019/05/cisco-vni-feb2019.pdf, Accesed on 18 April 2021.
[8] Clark, D. D.; Partridge, C.; Ramming J. C.; Wrocławski, J. T. (2003). A knowledge Plane for the Internet, Proc SIGCOMM'03, 3-10, 2003. https://doi.org/10.1145/863955.863957
[9] Dias, K. L.; Pongelupe, M. A.; Caminhas, W. M.; de Errico, L. (2019). An innovative approach for real-time network traffic classification, Computer Networks, 158, 143--157, 2019. https://doi.org/10.1016/j.comnet.2019.04.004
[10] Dhote, Y.; Agrawal S.; Deen, A. J. (2015). A Survey on Feature Selection Techniques for Internet Traffic Classification, International Conference on Computational Intelligence and Communication Networks, 1375-1380, 2015. https://doi.org/10.1109/CICN.2015.267
[11] Duan, M. (2018). Short-Time Prediction of Traffic Flow Based on PSO Optimized SVM, International Conference on Intelligent Transportation, Big Data & Smart City, 41-45, 2018. https://doi.org/10.1109/ICITBS.2018.00018
[12] Duque-Torres, A.; Amezquita-Suárez, F.; Caicedo-Rendon, O. M.; Ordóñez A.; Campo, W. Y. (2019). An Approach Based on Knowledge-Defined Networking for Identifying Heavy-Hitter Flows in Data Center Networks, Applied Sciences, 9(22):4808, 2019. https://doi.org/10.3390/app9224808. https://doi.org/10.3390/app9224808
[13] Duque-Torres, A.; Pekar, A.; Seah, W. K. G.; Caicedo-Rendon, O. M. (2019). Heavy- Hitter Flow Identification in Data Centre Networks Using Packet Size Distribution and Template Matching, IEEE 44th Conference on Local Computer Networks, 10-17, doi: 10.1109/LCN44214.2019.8990807, 2019. https://doi.org/10.1109/LCN44214.2019.8990807
[14] Ericsson, Ericsson Mobility Report, June, pp. 36, 2020. [Online]. Available: https://www.ericsson.com/49da93/assets/local/mobility-report/documents/2020/june2020- ericsson- mobility- report.pdf, Accesed on 1 May 2021.
[15] Finsterbusch, M.; Richter, C.; Rocha, E.; Muller, J.; Hanssgen, K. (2014). A Survey of Payload- Based Traffic Classification Approaches, IEEECommunications Surveys Tutorials, 16(2), 1135- 1156, 2014. https://doi.org/10.1109/SURV.2013.100613.00161
[16] Hayes, M.; Ng, B.; Pekar, A.; Seah, W. K. G. (2018). Scalable Architecture for SDN Traffic Classification, IEEE Systems Journal, 12(4), 3203-3214, 2018. https://doi.org/10.1109/JSYST.2017.2690259
[17] Hou, Y.; Huang, H.; Shao, W. (2014). Traffic Classification Method by Combination of Host Behaviour and Statistical Approach, Journal of Engineering Science and Technology Review, 7, 151-157, 2014. https://doi.org/10.25103/jestr.073.24
[18] Huber, S.; Wiemer, H.; Schneider, D.; Ihlenfeldt, S. (2019). DMME: Data mining methodology for engineering applications - a holistic extension to the CRISP-DM model, 12th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 79, 403-408, 2019. https://doi.org/10.1016/j.procir.2019.02.106
[19] Hyun, J.; Tu, N. V.; Hong, J. (2017). Knowledge-defined networking using in-band network telemetry, 19th AsiaPacific Network Operations and Management Symposium, 54-57, 2017. https://doi.org/10.1109/APNOMS.2017.8094178
[20] Kreutz, D.; Ramos, F. M. V.; Veríssimo, P. E.; Rothenberg, C. E.; Azodolmolky S.; Uhlig S. (2015). Software-Defined Networking: A Comprehensive Survey, Proceedings of the IEEE, 103(1), 14-76, 2015. https://doi.org/10.1109/JPROC.2014.2371999
[21] Lopes Pereira, S.; De Castro e Silva, J. L.; Bessa Maia, J. E. (2014). Ntcs: A real time flow-based network traffic classification system, 10th International Conference on Network and ServiceManagement, 368-371, 2014.
[22] Mestres, A.; Rodriguez-Natal, A.; J. Carner, J.; Barlet-Ros, P.; Alarcón E. et al (2017). Knowledge-Defined Networking, SIGCOMM Comput. Commun, 47(3), 2-10, 2017. https://doi.org/10.1145/3138808.3138810
[23] NFStream, Flexible Network Data Analysis Framework, 2020. [Online]. Available: https://www.nfstream.org/, Accesed on 10 May 2021.
[24] OpenDaylight, OpenDaylight Controller, 2020. [Online]. Available: https://www.opendaylight.org/ Accesed on 10 May 2021.
[25] Ordonez-Lucena, J.; Ameigeiras, P.; Lopez, D.; Ramos-Munoz, J. J. et al. (2017). Network Slicing for 5G with SDN/NFV: Concepts, Architectures, and Challenges, IEEE Communications Magazine, 55(5), 80-87, 2017. https://doi.org/10.1109/MCOM.2017.1600935
[26] Pekar, A.; Chovanec, M.; Vokorokos, L.; Chovancova, E.; Fecilak, P.; Michalko, M (2018). Adaptive Aggregation of Flow Records, Computing and Informatics, 37, 142-164, 2018, doi: 10.4149/cai_2018_1_142. https://doi.org/10.4149/cai_2018_1_142
[27] Pekar, A.; Duque-Torres, A. (2018). A Network Traffic Flow Feature Measurement Tool - flowRecorder, version v1.1.2, Nov. 2018.
[28] Pekar, A.; Duque-Torres, A.; Seah, W. K. G. et al (2021). Knowledge Discovery: Can It Shed New Light on Threshold Definition for Heavy-Hitter Detection?, J Netw Syst Manage, 29, 24, 2021, https://doi.org/10.1007/s10922-021-09593-w. https://doi.org/10.1007/s10922-021-09593-w
[29] Reza, M.; Sobouti, M. J.; Raouf, S.; Javidan, R. (2017). Network traffic classification using machine learning techniques over software definednetworks, International Journal of Advanced Computer Science and Applications, 8, 2017. https://doi.org/10.14569/IJACSA.2017.080729
[30] Sandvine, Global Internet Phenomena Report 2019, June, pp. 23, 2019. [Online]. Available: https://www.sandvine.com/global-internet-phenomena-report-2019, Accesed on 15 May 2021.
[31] Scikit-learn, Machine Learning in Python, 2020. [Online]. Available: https://scikitlearn. org/stable/, Accesed on 18 December 2020.
[32] Subasi, A. (2020). Practical Machine Learning for Data Analysis Using Python || Machine learning techniquesr, 91-202, doi:10.1016/B978-0-12-821379-7.00003-5, 2020. https://doi.org/10.1016/B978-0-12-821379-7.00003-5
[33] Van Asten, B. J.; van Adrichem, N. L. M.; Kuipers, F. A. (2014). Scalability and Resilience of Software-Defined Networking: An Overview, 1408.6760, 2014
[34] VLC, VLC media player, 2021. [Online]. Available: https://www.videolan.org/vlc/index.es.htmlAccesed on 11 September 2020.
[35] Wireshark, Wireshark tool, 2021. [Online]. Available: https://www.wireshark.org/#download, Accesed on 16 September 2020.
[36] Wowza, Wowza Streaming Engine, 2021. [Online]. Available: https://www.wowza.com/, Accesed on 13 April 2021.
[37] Zodiac, The world's smallest OpenFlow SDN switch, 2020. [Online]. Available: https://northboundnetworks.com/pages/zodiac-fx-faq., Accesed on 14 April 2021.
[38] Zou, X.; Hu, Y.; Tian, Z.; Shen, K. (2019). Logistic regression model optimization and case analysis, IEEE 7th International Conference on Computer Science and Network Technology, 135- 139, 2019. https://doi.org/10.1109/ICCSNT47585.2019.8962457
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