Comparación de la eficiencia de algoritmos de ML supervisados en la clasificación de tráfico NFV
DOI:
https://doi.org/10.18046/syt.v15i42.2539Palabras clave:
Algoritmos supervisados, aprendizaje de máquina, NFV, tráfico IP.Resumen
La implementación de NFV permite mejorar la flexibilidad, eficiencia y gestión de redes al emplear tecnologías de virtualización y computación en la nube para desplegar nuevas redes de computadores. La implementación de procesos de gestión autónomos, junto con algoritmos de aprendizaje supervisado en la rama del conocimiento denominada aprendizaje de máquina (ML, Machine Learning) se ha convertido en una estrategia clave para gestionar tráfico en segundo plano. En este documento se presenta un proyecto de investigación que analiza características de tráfico de redes basadas en NFV al realizar una comparativa de la eficiencia (benchmarking) del comportamiento de algoritmos de aprendizaje supervisado para ML. Se analizaron los algoritmos J48, Naïve Bayes y Bayes Net y se analizó la clasificación de tráfico IP respecto a su eficiencia, la que está relacionada con la compensación entre el tiempo de respuesta y la precisión del algoritmo. Se emplearon dos escenarios de prueba (una SDN basada en NFV y un EPC LTE basado en NFV). Los resultados del benchmarking revelan que los algoritmos Naïve Bayes y Bayes Net obtuvieron mejor desempeño en la clasificación del tráfico. En particular, estos valores corroboran una adecuada compensación entre precisión y tiempo de respuesta, con valores de precisión mayores a 80% y 96%, respectivamente, en tiempos menores a 1.5 segundos.
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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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Publicado
2017-10-19
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Investigación científica y tecnológica
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Esta publicación está licenciada bajo los términos de la licencia CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/deed.es)
