prikaz prve stranice dokumenta Procjena performansi usluge strujanja sadržaja uživo putem platforme YouTube na osnovu analize kriptiranog prometa uporabom metoda strojnog učenja
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undergraduate thesis
Procjena performansi usluge strujanja sadržaja uživo putem platforme YouTube na osnovu analize kriptiranog prometa uporabom metoda strojnog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. urn:nbn:hr:168:320010
Matić, Filip
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Matić, F. (2018). Procjena performansi usluge strujanja sadržaja uživo putem platforme YouTube na osnovu analize kriptiranog prometa uporabom metoda strojnog učenja (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:320010

Matić, Filip. "Procjena performansi usluge strujanja sadržaja uživo putem platforme YouTube na osnovu analize kriptiranog prometa uporabom metoda strojnog učenja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:320010

Matić, Filip. "Procjena performansi usluge strujanja sadržaja uživo putem platforme YouTube na osnovu analize kriptiranog prometa uporabom metoda strojnog učenja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:320010

Matić, F. (2018). 'Procjena performansi usluge strujanja sadržaja uživo putem platforme YouTube na osnovu analize kriptiranog prometa uporabom metoda strojnog učenja', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 08 January 2025, https://urn.nsk.hr/urn:nbn:hr:168:320010

Matić F. Procjena performansi usluge strujanja sadržaja uživo putem platforme YouTube na osnovu analize kriptiranog prometa uporabom metoda strojnog učenja [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2018 [cited 2025 January 08] Available at: https://urn.nsk.hr/urn:nbn:hr:168:320010

F. Matić, "Procjena performansi usluge strujanja sadržaja uživo putem platforme YouTube na osnovu analize kriptiranog prometa uporabom metoda strojnog učenja", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2018. Available at: https://urn.nsk.hr/urn:nbn:hr:168:320010

Metadata
TitleProcjena performansi usluge strujanja sadržaja uživo putem platforme YouTube na osnovu analize kriptiranog prometa uporabom metoda strojnog učenja
Title (english)A Machine Learning Approach for Performance Estimation of Live YouTube Streaming Based on the Analysis of Encrypted Network Traffic
AuthorFilip Matić
MentorLea Skorin-Kapov (mentor)
Committee memberLea Skorin-Kapov (predsjednik povjerenstva)
Committee memberMirko Sužnjević (član povjerenstva)
Committee memberIrena Oršolić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2018-07-13, 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
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:320010
Study programmeTitle: 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 resourceText
File originBorn digital
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Created on2019-03-13 22:50:34