Naslov
YouTube performance estimation based on the analysis of encrypted network traffic using machine learning

Autori
Oršolić, Irena

Vrsta, podvrsta i kategorija rada
Ocjenski radovi, diplomski rad, diplomski

Fakultet
Fakultet elektrotehnike i računarstva

Mjesto
Zagreb

Datum
07.07

Godina
2016

Stranica
91

Mentor
Skorin-Kapov, Lea

Ključne riječi
network traffic classification ; machine learning ; Quality of Experience ; YouTube

Sažetak
This thesis was completed in the scope of a project focused on YouTube QoE prediction based on encrypted network traffic. During this project, a system called YouQ was developed for the purpose of collecting data both on a client device (Android smartphone) and in the network. The system also includes the YouQ server for processing and visualizing data. This thesis focuses on the second phase of the project: using machine learning algorithms on data collected by the YouQ system to predict QoE classes of each YouTube video streaming session. The dataset that was analysed consists of 343 instances described with 54 network traffic features and a QoE class label (“high”, “medium”, “low”). The dataset was subset using wrapper methods in the WEKA machine learning tool for four machine learning algorithms: Naïve Bayes, SMO, J48, and Random Forest. Classification models were created using OneR and the four listed algorithms. Classification accuracy was assessed and discussed. The results show that the most accurate was the model created using the Random Forest algorithm, with 74% accuracy. Most of the errors happened between classes “low” and “medium” and in the experiments with bandwidth fluctuations. Besides presenting the methodology of feature manipulation, model creation, and evaluation, this thesis provides theoretical information about algorithms, a short guide to using WEKA, and statistic analysis of both application-level and network-level data. Further, to better understand the dataset that was used as an input to machine learning algorithms, the experiment setup and data collection methodology has been described.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

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