Autonomic classification of IP traffic in an NFV-based network (2017)
Network Function Virtualization (NFV) is an emerging solution that improves the ﬂexibility, eﬃciency, and manageability of networks by leveraging virtualization and cloud computing technologies to run networked devices in software. The implemen-tation of NFV presents issues such as the introduction of new software components, bottleneck performance and monitoring of hidden traﬃc. A considerable amount of NFV traﬃc is invisible using traditional monitoring strategies because it does not hit a physical link. The implementation of autonomous management and supervised algorithms of Machine Learning (ML) become a key strategy to manage this hidden traﬃc. In this undergraduate work, we focus on analyzing NFV traﬃc features in two test environments with diﬀerent components and traﬃc generation. We perform a benchmarking of the performance of supervised ML algorithms concerning its eﬃciency; considering that the eﬃciency of the algorithms depends on the trade-oﬀ between the time-response and the precision achieved in the classiﬁcation. The results show that the NaiveBayes and C4.5 algorithms reach values greater than 90.68 % in a response time range between 0.37 sec and 3 sec.