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Pal, Data Mining: Practical machine learning tools and techniques, Morgan Kaufmann, 2016."},{"key":"8","doi-asserted-by":"publisher","unstructured":"[9] J. Schmidhuber, \u201cDeep learning in neural networks: An overview,\u201d Neural Networks, vol.61, pp.85-117, 2015. 10.1016\/j.neunet.2014.09.003","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[10] A. Nakao, \u201cNetwork virtualization as foundation for enabling new network architectures and applications,\u201d IEICE Trans. Commun., vol.E93-B, no.3, pp.454-457, March 2010. 10.1587\/transcom.e93.b.454","DOI":"10.1587\/transcom.E93.B.454"},{"key":"10","unstructured":"[11] \u201cPlanetlab,\u201d http:\/\/www.planet-lab.org, 2012."},{"key":"11","unstructured":"[12] \u201cPlanetlab,\u201d https:\/\/www.planet-lab.eu, 2012."},{"key":"12","unstructured":"[13] \u201cGeni,\u201d https:\/\/www.geni.net, 2007."},{"key":"13","doi-asserted-by":"crossref","unstructured":"[14] K. Yamada, Y. Kanada, K. Amemiya, A. Nakao, and Y. Saida, \u201cVnode infrastucture enhancement \u2014 Deeply programmable network virtualization,\u201d 2015 21st Asia-Pacific Conference on Communications (APCC), IEEE, pp.244-249, 2015. 10.1109\/apcc.2015.7412518","DOI":"10.1109\/APCC.2015.7412518"},{"key":"14","unstructured":"[15] \u201cFlare: Open deeply programmable network node architecture,\u201d http:\/\/netseminar.stanford.edu\/seminars\/10_18_12.pdf"},{"key":"15","unstructured":"[16] \u201cFed4fire,\u201d https:\/\/www.fed4fire.eu"},{"key":"16","unstructured":"[17] 5GMF White Paper, \u201c5G mobile communictions systems for 2020 and beyond,\u201d http:\/\/5gmf.jp\/wp\/wp-content\/uploads\/2016\/09\/5GMF_WP101_All.pdf, July 2016."},{"key":"17","doi-asserted-by":"crossref","unstructured":"[18] P. Du and A. Nakao, \u201cApplication specific mobile edge computing through network softwarization,\u201d IEEE International Conference on Cloud Networking (CloudNet), 2016. 10.1109\/cloudnet.2016.54","DOI":"10.1109\/CloudNet.2016.54"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[19] A. Nakao, P. Du, Y. Kiriha, F. Granelli, A.A. Gebremariam, T. Taleb, and M. Bagaa, \u201cEnd-to-end network slicing for 5G mobile networks,\u201d Journal of Information Processing, vol.25, pp.153-163, 2017. 10.2197\/ipsjjip.25.153","DOI":"10.2197\/ipsjjip.25.153"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[20] A. Callado, C. Kamienski, G. Szab\u00f3, B.P. Gero, J. Kelner, S. Fernandes, and D. Sadok, \u201cA survey on internet traffic identification,\u201d IEEE Commun. Surveys Tuts., vol.11, no.3, pp.37-52, 2009. 10.1109\/surv.2009.090304","DOI":"10.1109\/SURV.2009.090304"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[21] M. Cotton, L. Eggert, J. Touch, M. Westerlund, and S. Cheshire, \u201cInternet assigned numbers authority (IANA) procedures for the management of the service name and transport protocol port number registry,\u201d RFC 6335, Aug. 2011. 10.17487\/RFC6335","DOI":"10.17487\/rfc6335"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[22] J. Van Der Merwe, R. Caceres, Y.-H. Chu, and C. Sreenan, \u201cmmdump: A tool for monitoring internet multimedia traffic,\u201d ACM SIGCOMM Comput. Commun. Rev., vol.30, no.5, pp.48-59, 2000. 10.1145\/505672.505678","DOI":"10.1145\/505672.505678"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[23] S. Sen, O. Spatscheck, and D. Wang, \u201cAccurate, scalable in-network identification of P2P traffic using application signatures,\u201d Proc. 13th international conference on World Wide Web. ACM, pp.512-521, 2004. 10.1145\/988672.988742","DOI":"10.1145\/988672.988742"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[24] J. Erman, A. Mahanti, and M. Arlitt, \u201cQrp05-4: Internet traffic identification using machine learning,\u201d Global Telecommunications Conference, 2006. GLOBECOM'06, IEEE, pp.1-6, IEEE, 2006. 10.1109\/glocom.2006.443","DOI":"10.1109\/GLOCOM.2006.443"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[25] S. Zander, T. Nguyen, and G. Armitage, \u201cSelf-learning IP traffic classification based on statistical flow characteristics,\u201d Passive and Active Network Measurement, pp.325-328, 2005. 10.1007\/978-3-540-31966-5_26","DOI":"10.1007\/978-3-540-31966-5_26"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[26] D. Bonfiglio, M. Mellia, M. Meo, D. Rossi, and P. Tofanelli, \u201cRevealing skype traffic: When randomness plays with you,\u201d ACM SIGCOMM Comput. Commun. Rev., vol.37, no.4, pp.37-48, 2007. 10.1145\/1282427.1282386","DOI":"10.1145\/1282427.1282386"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[27] J. Erman, A. Mahanti, M. Arlitt, and C. 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