A benchmarking of the efficiency of supervised ML algorithms in the NFV traffic classification

  • Juliana Alejandra Vergara Reyes Universidad del Cauca
  • María Camila Martínez Ordoñez Universidad del Cauca
  • Oscar Mauricio Caicedo Rendón Universidad del Cauca
Keywords: IP traffic, NFV, machine learning, supervised algorithms.


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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Author Biographies

Juliana Alejandra Vergara Reyes, Universidad del Cauca

Electronics and Telecommunications Engineer from the Universidad del Cauca (Popayán, Colombia). She has made emphasis in Telecommunications on her bachelor studies. She is an ISOC and IEEE Communications Society member. Her main interest is oriented to NFV, network management, control systems and related works to telecommunications engineering


María Camila Martínez Ordoñez, Universidad del Cauca

Electronics and Telecommunications Engineer from the Universidad del Cauca (Popayán, Colombia). She has made emphasis in Telecommunications on her bachelor studies. She is an ISOC and IEEE Communications society member. Her main interest is oriented to NFV, network management, optical fiber networks, wireless communications and related works to telecommunications engineering

Oscar Mauricio Caicedo Rendón, Universidad del Cauca

Full professor at the Telematics Department at the Universidad del Cauca [Unicauca] (Popayán, Colombia). As researcher, he is part of the Telematics Engineering Group at Unicauca and the Computer Networks Group at Universidade Federal do Rio Grande do Sul [UFRGS] (Porto Alegre, Brasil). He holds a Ph.D. in Computer Science from the Institute of Informatics of UFRGS, a Master in Telematics and a Bachelor in Telecommunications from Unicauca. He has been an IETF Fellowship and a traveler grant from ACM Sigcomm. Furthermore, he has published in prominent journals as Computer Networks and Computer Communications, and in relevant conferences as IEEE Globecom, AINA, COMPSAC, ISCC, and CNSM 


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