Procjena performansi usluge strujanja sadržaja uživo putem platforme YouTube na osnovu analize kriptiranog prometa uporabom metoda strojnog učenja

undergraduate thesis

• Title (english): A Machine Learning Approach for Performance Estimation of Live YouTube Streaming Based on the Analysis of Encrypted Network Traffic
• Author: Filip Matić
• Mentor: Lea Skorin-Kapov (mentor)
• Committee member: Lea Skorin-Kapov (predsjednik povjerenstva)
• Committee member: Mirko Sužnjević (član povjerenstva)
• Committee member: Irena Oršolić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2018-07-13, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Danas dominantni udio ukupnog prometa na Internetu čine usluge video strujanja. Većina tog prometa se temelji na prilagodljivom strujanju putem protokola HTTP. Jedna od najpopularnijih platformi za dijeljenje takvog sadržaja je YouTube. U okviru ovog rada će se posebno razmatrati usluga strujanja sadržaja uživo putem YouTube-a na Android mobilnim uređajima.
Davatelji mrežnih usluga nemaju uvid u performanse i parametre kvalitete video tokova koji prolaze njihovom mrežom jer je promet najčešće kriptiran. To mrežnim operaterima predstavlja veliki problem zbog nemogućnosti praćenja iskustvene kvalitete sadržaja koji isporučuju korisnicima. Zadatak ovog rada je procijeniti performanse na razini aplikacije na temelju prometnih značajki koristeći metode strojnog učenja. U istraživanju koje je provedeno u sklopu ovog rada su se prikupljali podaci mrežne i aplikacijske razine za 100 videa. Prikupljeni podaci čine ulazni skup podataka za algoritme strojnog učenja. Kao rezultat analize su dobiveni modeli na temelju kojih se parametri aplikacijske razine mogu procijeniti sa točnošću oko 60-70%.

Abstract (english)

Nowdays largest amount of Internet traffic belongs to video streaming services. Most of that traffic is delivered via the Adaptive Streaming over HTTP. One of the most popular platform for sharing that kind of content is YouTube. This thesis particulary analyzes live streaming on YouTube on Android smartphones.
Network operators generally lack insight into application-level quality indicators because most of the traffic is encrypted. This is big problem for ISPs due to the inability to monitor end-user Quality of Experience. Main objective of this thesis is to estimate performance of live YouTube streaming based on the analysis of encrypted network traffic using machine learning. In research conducted for the purposes of this thesis data of aplication-level quality indicators and corresponding traffic traces has been collected for 100 YouTube videos. Collected data is then used for the development of machine learning models for QoE classification based on computed traffic features. As a result of the analysis, classification accuracy was found to be about 60-70%.

• Keywords: strujanje sadržaja uživo; iskustvena kvaliteta; YouTube; strojno učenje
• Keywords (english): Live streaming; Quality of Experience; YouTube; Machine learning
• Language: croatian
• URN:NBN: urn:nbn:hr:168:320010
• Study programme: Title: Computing; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2019-03-13 22:50:34