An Approach Based on Knowledge-Defined Networking for Identifying Video Streaming Flows in 5G Networks
Keywords:
5G, Flow Classification, KDN, SDN, Video StreamingAbstract
5G aims to provide a complete wireless communication system with various applications, network services and technologies. In terms of 5G network management, Software-Defined Networking (SDN), and Network Functions Virtualization (NFV)are expected to control and manage network resources. Network Softwarization provides better management of network traffic. However, it does not guarantee network performance will not degradation when the traffic rises. Flow identification has been raised as a solution for keeping the network performance, and it has become a hot topic in both, academy and industry. In particular, there is a high interest in identifying video streaming flows since thanks to 5G and its benefits that improve the streaming media industry, the video streaming traffic is expected to increase dramatically due to the massive connection of 5G compatible devices. Motivated by this, we presented a novel approach for identifying video streaming services. Our approach includes three modules: video stream acquisition module, video stream analyzer module, and application module. In the video stream acquisition module, we capture video streaming packets and organize them in to flow records. In the video streaming analyzer module, we analyze the flow records using supervised machine learning algorithms to find the appropriate algorithm that performs better. In the application module, we provide a brief explanation of the applications of our approach. Additionally, we provide an analysis of the overall workload generated by our approach. The results of the evaluation by module corroborate the usefulness and feasibility of our approach for identifying video streaming services.
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L. Bonati, M. Polese, S. D’Oro, S. Basagni y T. Melodia, “Open, Programmable, and Virtualized 5G Networks: State-of-the-Art and the Road Ahead”, Computer Networks, vol. 182, p´ag. 107 516, 2020.
N. Slamnik-Krijeˇstorac, H. Kremo, M. Ruffini y J. M. Marquez-Barja, “Sharing Distributed and Heterogeneous Resources toward End-to-End 5G Networks: A Comprehensive Survey and a Taxonomy”, IEEE Communications Surveys Tutorials, vol. 22, n.o 3, p´ags. 1592-1628, 2020.
L. V. Le, B.-S. Lin y D. Sinh, “Applying Big Data, Machine Learning, and SDN/NFV for 5G Early-Stage Traffic Classification and Network QoS Control”, Transactions on Networks and Communications, vol. 6, abr. de 2018.
P. Pirinen, “A brief overview of 5G research activities”, en 1st International Conference on 5G for Ubiquitous Connectivity, 2014, p´ags. 17-22.
J. Ordonez-Lucena, P. Ameigeiras, D. Lopez, J. J. Ramos-Munoz, J. Lorca y J. Folgueira, “Network Slicing for 5G with SDN/NFV: Concepts, Architectures, and Challenges”, IEEE Communications
Magazine, vol. 55, n.o 5, p´ags. 80-87, 2017.
B. J. van Asten, N. L. M. van Adrichem y F. A. Kuipers, Scalability and Resilience of Software-Defined Networking: An Overview, 2014.
M. Condoluci y T. Mahmoodi, “Softwarization and virtualization in 5G mobile networks: Benefits, trends and challenges”, Computer Networks, vol. 146, sep. de 2018.
D. Awduche, A. Chiu, A. Elwalid, I. Widjaja y X. Xiao, “Overview and Principles of Internet Traffic Engineering”, RFC, vol. 3272,n p´ags. 1-71, 2002.
A. Callado, C. Kamienski, G. Szabo, B. P. Gero, J. Kelner, S. Fernandes y D. Sadok, “A Survey on Internet Traffic Identification”, IEEE Communications Surveys Tutorials, vol. 11, n.o 3, p´ags. 37-52, 2009.
M. Hayes, B. Ng, A. Pekar y W. K. G. Seah, “Scalable Architecture for SDN Traffic Classification”, IEEE Systems Journal, vol. 12, n.o 4, p´ags. 3203-3214, 2018.
H. Feng-Hui, Z. Wen-An y D. Yu, “QoE Issues of OTT Services over 5G Network”, en 2014 Ninth International Conference on Broadband and Wireless Computing, Communication and Applications, 2014, p´ags. 267-273.
Cisco, “Cisco Visual Networking Index: Forecast and Trends, 2017–2022”, Cisco, n.o Feb, p´ag. 147, 2019, [Online]. Available: https://davidellis.ca/wp-content/uploads/2019/05/cisco-vni-feb2019.pdf.
Sandvine, “Global Internet Phenomena Report 2019”, Sandvine, p´ag. 23, 2019, [Online]. Available:https://www.sandvine.com/globalinternet-phenomena-report-2019.
Ericcson, “Ericsson Mobility Report”, Ericsson, n.o June, p´ag. 36, 2020, [Online.] Available: https://www.ericsson.com/49da93/assets/local/mobility- report/documents/2020/june2020- ericsson- mobilityreport.pdf.
X. Foukas, G. Patounas, A. Elmokashfi y M. K. Marina, “Network Slicing in 5G: Survey and Challenges”, IEEE Communications Magazine, vol. 55, n.o 5, p´ags. 94-100, 2017.
Y. Dhote, S. Agrawal y A. J. Deen, “A Survey on Feature Selection Techniques for Internet Traffic Classification”, en 2015 International Conference on Computational Intelligence and Communication Networks (CICN), 2015, p´ags. 1375-1380.
M. Finsterbusch, C. Richter, E. Rocha, J. Muller y K. Hanssgen, “A Survey of Payload-Based Traffic Classification Approaches”, IEEE Communications Surveys Tutorials, vol. 16, n.o 2, p´ags. 1135-1156, 2014.
Y. Hou, H. Huang y W. Shao, “Traffic Classification Method by Combination of Host Behaviour and Statistical Approach”, Journal of Engineering Science and Technology Review, vol. 7, p´ags. 151-157, jul. de 2014.
T. Bakhshi, “Multi-feature Enterprise Traffic Characterization in OpenFlow-based Software Defined Networks”, en 2017 International Conference on Frontiers of Information Technology (FIT), 2017, p´ags. 23-28.
R. Boutaba, M. Salahuddin, N. Limam, S. Ayoubi, N. Shahriar, F. Estrada-Solano y O. Caicedo Rendon, “A Comprehensive Survey on Machine Learning for Networking: Evolution, Applications and Research Opportunities”, Journal of Internet Services and Applications, vol. 9, mayo de 2018.
D. D. Clark, C. Partridge, J. C. Ramming y J. T. Wroclawski, “A Knowledge Plane for the Internet”, ´ep. SIGCOMM ’03, Karlsruhe, Germany: Association for Computing Machinery, 2003.
C. M. Bishop y N. Nasrabadi, “Pattern Recognition and Machine Learning”, J. Electronic Imaging, vol. 16, p´ag. 049 901, 2007.
A. Pekar y A. Duque, “A Network Traffic Flow Feature Measurement Tool”, (2018, Nov). v1.1.2 [Online]. Available:https://github.com/drnpkr/flowRecorder2018.
N. Developers, “Flexible Network Data Analysis Framework”, (2020, Nov. 15). v6.2.3 [Online]. Available: https://www.nfstream.org/.
K. L. Dias, M. A. Pongelupe, W. M. Caminhas y L. de Errico, “An innovative approach for real-time network traffic classification”, Computer Networks, vol. 158, p´ags. 143 -157, 2019.
J. A. Wickboldt, W. P. De Jesus, P. H. Isolani, C. B. Both, J. Rochol y L. Z. Granville, “Software-defined networking: management requirements and challenges”, IEEE Communications Magazine, vol. 53, n.o 1, p´ags. 278-285, 2015.
F. Estrada-Solano, A. Ordonez, L. Z. Granville y O. M. Caicedo Rendon, “A framework for SDN integrated management based on a CIM model and a vertical management plane”, Computer Communications, vol. 102, p´ags. 150 -164, 2017.
D. Kreutz, F. M. V. Ramos, P. E. Ver´ıssimo, C. E. Rothenberg, S. Azodolmolky y S. Uhlig, “Software-Defined Networking: A Comprehensive Survey”, Proceedings of the IEEE, vol. 103, n.o 1, p´ags. 14-76, 2015.
A. Mestres, A. Rodriguez-Natal, J. Carner, P. Barlet-Ros, E. Alarc´on, M. Sol´e, V. Munt´es-Mulero, D. Meyer, S. Barkai, M. J. Hibbett, G. Estrada, K. Ma’ruf, F. Coras, V. Ermagan, H. Latapie, C. Cassar, J. Evans, F. Maino, J. Walrand y A. Cabellos, “Knowledge-Defined Networking”, SIGCOMM Comput. Commun. Rev., vol. 47, n.o 3, 2–10, sep. de 2017.
T. T. T. Nguyen y G. Armitage, “A survey of techniques for internet traffic classification using machine learning”, IEEE Communications Surveys Tutorials, vol. 10, n.o 4, p´ags. 56-76, 2008.
P. Bermolen y D. Rossi, “Support vector regression for link load prediction”, en 2008 4th International Telecommunication Networking Workshop on QoS in Multiservice IP Networks, 2008, p´ags. 268-273.
A. Lakhina, M. Crovella y C. Diot, “Mining anomalies using traffic feature distributions”, en In ACM SIGCOMM, 2005, p´ags. 217-228.
J. Zhang, Y. Xiang, W. Zhou e Y. Wang, “Unsupervised traffic classification using flow statistical properties and IP packet payload”, Journal of Computer and System Sciences, vol. 79, n.o 5, p´ags. 573-585, 2013.
W. Iqbal, M. N. Dailey y D. Carrera, “Unsupervised Learning of Dynamic Resource Provisioning Policies for Cloud-Hosted Multitier Web Applications”, IEEE Systems Journal, vol. 10, n.o 4,
p´ags. 1435-1446, 2016.
J. Hyun, N. V. Tu y J. Hong, “Knowledge-defined networking using in-band network telemetry”, 2017 19th Asia-Pacific Network Operations and Management Symposium (APNOMS), p´ags. 54-57, 2017.
T. En-Najjary, “Traffic classification : Application-based feature selection using logistic regression”, 2010.
S. Santiago Lopes Pereira, J. L. De Castro e Silva y J. E. Bessa Maia, “NTCS: A real time flow-based network traffic classification system”, en 10th International Conference on Network and Service Management (CNSM) and Workshop, 2014, p´ags. 368-371.
A. Pradhan, “Network Traffic Classification using Support Vector Machine and Artificial Neural Network”, oct. de 2011.
M. Reza, M. J. Sobouti, S. Raouf y R. Javidan, “Network Traffic Classification using Machine Learning Techniques over Software Defined Networks”, International Journal of Advanced Computer Science and Applications, vol. 8, ene. de 2017.
R. Nossenson y S. Polacheck, “On-Line Flows Classification of Video Streaming Applications”, en 2015 IEEE 14th International Symposium on Network Computing and Applications, 2015, p´ags. 251-258.
O. Project, “OpenDaylight Controller”, [Online]. Available: https://www.opendaylight.org/.
L. Casta˜neda, “Datavideo, C´odigo para la comparaci´on de rendimiento de diferentes modelos de ML para videostreaming”, (2020, Nov). [Online]. Available:
https: //github.com/luisingeniero11/datavideo.
