A benchmarking of the efficiency of supervised ML algorithms in the NFV traffic classification
DOI:
https://doi.org/10.18046/syt.v15i42.2539Keywords:
IP traffic, NFV, machine learning, supervised algorithms.Abstract
The implementation of NFV allows improving the flexibility, efficiency, and manageability of networks by leveraging virtualization and cloud computing technologies to deploy computer networks. The implementation of autonomic management and supervised algorithms from Machine Learning [ML] become a key strategy to manage this hidden traffic. In this work, we focus on analyzing the traffic features of NFV-based networks while performing a benchmarking of the behavior of supervised ML algorithms, namely J48, Naïve Bayes, and Bayes Net, in the IP traffic classification regarding their efficiency; considering that such an efficiency is related to the trade-off between time-response and precision. We used two test scenarios (an NFV-based SDN and an NFV-based LTE EPC). The benchmarking results reveal that the Naïve Bayes and Bayes Net algorithms achieve the best performance in traffic classification. In particular, their performance corroborates a good trade-off between precision and time-response, with precision values higher than 80 % and 96 %, respectively, in times less than 1,5 sec.
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Zhu, J. (2014). Quantitative models for performance evaluation and benchmarking: data envelopment analysis with spreadsheets. New York, NY: Springer.
Botta, A., Dainotti, A., & Pescapé, A. (2012). A tool for the generation of realistic network workload for emerging networking scenarios. Computer Networks, 56(15), 3531-3547.
Bujlow, T., Riaz, T., & Pedersen, J. M. (2012, January). A method for classification of network traffic based on C5. 0 Machine Learning Algorithm. In Computing, Networking and Communications (ICNC), 2012 International Conference on (pp. 237-241). IEEE.
Carela-Español, V., Barlet-Ros, P., Mula-Valls, O., & Sole-Pareta, J. (2015). An autonomic traffic classification system for network operation and management. Journal of Network and Systems Management, 23(3), 401-419.
Chapelle, O., Haffner, P., & Vapnik, V. (1999). Support vector machines for histogram-based image classification. IEEE transactions on Neural Networks, 10(5), 1055-1064.
Chi, P. W., Huang, Y. C., & Lei, C. L. (2015, June). Efficient NFV deployment in data center networks. In Communications (ICC), 2015 IEEE International Conference on (pp. 5290-5295). IEEE.
Chishti, H. R. (2013). A traffic classification method using machine learning algorithm [thesis]. Luton, UK: University of Bedfordshire.
Choudhury, S., & Bhowal, A. (2015). Comparative analysis of machine learning algorithms along with classifiers for network intrusion detection. In Smart Technologies and Management for Computing, Communication,
Controls, Energy and Materials (ICSTM), 2015 International Conference on (pp. 89-95). IEEE.
Cotroneo, D., De Simone, L., Iannillo, A. K., Lanzaro, A., Natella, R., Fan, J., & Ping, W. (2014). Network function virtualization: Challenges and directions for reliability assurance. In Software Reliability Engineering Workshops (ISSREW), 2014 IEEE International Symposium on (pp. 37-42). IEEE
Firoozjaei, M. D., Jeong, J. P., Ko, H., & Kim, H. (2017). Security challenges with network functions virtualization. Future Generation Computer Systems, 67, 315-324.
Frank, E. (2010). Weka-A machine learning workbench for data mining. In Data mining and knowledge discovery handbook (pp. 1269-1277). Boston, MA: Springer.
Gray, K. (2016). Network function virtualization. Boston, MA: Morgan Kaufmann.
He, L., Xu, C., & Luo, Y. (2016). VTC: Machine learning based traffic classification as a virtual network function. In Proceedings of the 2016 ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization (pp. 53-56). New York, NY: ACM.
iPerf. (2017). iPerf - The ultimate speed test tool for TCP, UDP and SCTP. Retrieved from https://iperf.fr/
Ixia. (2016). Network function virtualization (nfv): 5 major risks. Retrieved from https://www.ixiacom.com/resources/network-function-virtualization-nfv-5-major-risks
Kephart, J. O. & Chess D. M. (2003). The vision of autonomic computing. Computer, 36(1), 41-50.
Kumar, J., Satapaphy, P., Sadagopan, N., & Vutukuru, M. (2016). Virtualized evolved eacket core for LTE networks. Retrieved from: https://github.com/networkedsystemsIITB/NFV_LTE_EPC
Li, W., Canini, M., Moore, A. W., & Bolla, R. (2009). Efficient application identification and the temporal and spatial stability of classification schema. Computer Networks, 53(6), 790-809.
Mearns, H., & Leaney, J. (2013, April). The use of autonomic management in multi-provider telecommunication services. In Engineering of Computer Based Systems (ECBS), 2013 20th IEEE International Conference and Workshops on the (pp. 129-138). IEEE.
Ma, W., Medina, C., & Pan, D. (2015, December). Traffic-aware placement of NFV middle boxes. In Global Communications Conference (GLOBECOM), 2015 IEEE (pp. 1-6). IEEE.
Maglogiannis, I. (Ed.) (2007). Emerging artificial intelligence applications in computer engineering: real word ai systems with applications in ehealth, hci, information retrieval and pervasive technologies. Amsterdam, The Netherlands: IOS.
Mininet: An instant virtual network on your laptop (or other PC). (2017). Retrieved from http://mininet.org
Muralidharan, V., & Sugumaran, V. (2012). A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis. Applied Soft Computing, 12(8), 2023-2029.
Novakovic, J. (2016). Toward optimal feature selec-tion using ranking methods and classi-fication algorithms. Yugoslav Journal of Operations Research, 21(1), 119-135.
OVS - Openv vSwitch. (n.d.). Retrieved from http://openvswitch.org/
Qin, D., Yang, J., Wang, J., & Zhang, B. (2011, September). IP traffic classification based on machine learning. In Communication Technology (ICCT), 2011 IEEE 13th International Conference on (pp. 882-886). IEEE.
RYU SDN framework. (2017). Retrieved from http://osrg.github.io/ryu/
Shafiq, M., Yu, X., Laghari, A. A., Yao, L., Karn, N. K., & Abdessamia, F. (2016a). Network traffic classification techniques and comparative analysis using machine learning algorithms. In Computer and Communications (ICCC), 2016 2nd IEEE International Conference on (pp. 2451-2455). IEEE.
Shafiq, M., Yu, X., Laghari, A., Yao, L., Karn, N., Abdesssamia, F., & Salahuddin, S. (2016b). We chat text and picture messages service flow traffic classification using machine learning Technique. In IEEE HPCC/SmartCity/DSS (pp. 58-62).
Shankara, U. (2007). Patent No. 20070220217. Bengalooru, IN.
Singh, K., Agrawal, S., & Sohi, B. S. (2013). A near real-time IP traffic classification using machine learning. International Journal of Intelligent Systems and Applications, 5(3), 83.
Solomon, B., Ionescu, D., Litoiu, M., & Iszlai, G. (2010, May). Designing autonomic management systems for cloud computing. In Computational Cybernetics and Technical Informatics (ICCC-CONTI), 2010 International Joint Conference on (pp. 631-636). IEEE.
Sugumaran, V. M. (2012). A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis. Applied Soft Computing, 12(8), 2023 - 2029.
Tsagkaris, K., Logothetis, M., Foteinos, V., Poulios, G., Michaloliakos, M., & Demestichas, P. (2015). Customizable autonomic network management: integrating autonomic network management and software-defined networking. IEEE Vehicular Technology Magazine, 10(1), 61-68.
Valdes, A., Macwan, R., & Backes, M. (2016). anomaly detection in electrical substation circuits via unsupervised machine learning. In Information Reuse and Integration (IRI), 2016 IEEE 17th International Conference on (pp. 500-505). IEEE.
VMware. (2017). Retrieved from https://www.vmware.com/
Weingärtner, R., Bräscher, G. B., & Westphall, C. B. (2016, June). A distributed autonomic management framework for cloud computing orchestration. In Services (SERVICES), 2016 IEEE World Congress on (pp. 9-17). IEEE.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Amsterdam, The Netherlands: Elsevier.
Zander, S., & Armitage, G. (2011, October). Practical machine learning based multimedia traffic classification for distributed QoS management. In Local Computer Networks (LCN), 2011 IEEE 36th Conference on (pp. 399-406). IEEE.
Zhu, J. (2014). Quantitative models for performance evaluation and benchmarking: data envelopment analysis with spreadsheets. New York, NY: Springer.
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