Francesca Fasano
Clustering for online video-conference traffic classification.
Rel. Michela Meo, Paolo Garza, Dena Markudova. Politecnico di Torino, Master of science program in Ict For Smart Societies, 2020
Abstract
With video, gaming and multimedia traffic representing almost 80% of total Internet traffic and RTC applications spread increasing each day more, the overall costumers expectation towards quality and performance of such services is also increasing. An emerging metric called Quality of Experience (QoE) has the goal to depict these qualitative aspects linked to users satisfaction and the development of new QoE driven network management frameworks is of key interest in industrial research. However, in order to take proper management actions the first problem to be solved is network traffic classification. Because of the increased sensibility towards privacy issues older methods such as port-based and Deep Packet Inspection are nowadays almost unusable, but recently machine learning techniques relying on statistical analysis of the flows have seen an important development.
Anyhow, supervised machine learning approaches usually require a costly and unfeasible labelling of the data, while fully unsupervised solutions could be of difficult interpretation
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