prikaz prve stranice dokumenta Procjena performansi usluge YouTube na osnovu analize kriptiranog prometa za 360 video na iOS platformi
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master's thesis
Procjena performansi usluge YouTube na osnovu analize kriptiranog prometa za 360 video na iOS platformi
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. urn:nbn:hr:168:252652
Šašić, Dunja
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Šašić, D. (2018). Procjena performansi usluge YouTube na osnovu analize kriptiranog prometa za 360 video na iOS platformi (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:252652

Šašić, Dunja. "Procjena performansi usluge YouTube na osnovu analize kriptiranog prometa za 360 video na iOS platformi." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:252652

Šašić, Dunja. "Procjena performansi usluge YouTube na osnovu analize kriptiranog prometa za 360 video na iOS platformi." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:252652

Šašić, D. (2018). 'Procjena performansi usluge YouTube na osnovu analize kriptiranog prometa za 360 video na iOS platformi', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 11 July 2024, https://urn.nsk.hr/urn:nbn:hr:168:252652

Šašić D. Procjena performansi usluge YouTube na osnovu analize kriptiranog prometa za 360 video na iOS platformi [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2018 [cited 2024 July 11] Available at: https://urn.nsk.hr/urn:nbn:hr:168:252652

D. Šašić, "Procjena performansi usluge YouTube na osnovu analize kriptiranog prometa za 360 video na iOS platformi", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2018. Available at: https://urn.nsk.hr/urn:nbn:hr:168:252652

Metadata
TitleProcjena performansi usluge YouTube na osnovu analize kriptiranog prometa za 360 video na iOS platformi
Title (english)YouTube Performance Estimation Based on the Analysis of Encrypted Network Traffic for 360 Video on the iOS platform
AuthorDunja Šašić
MentorLea Skorin-Kapov (mentor)
Committee memberLea Skorin-Kapov (predsjednik povjerenstva)
Committee memberOgnjen Dobrijević (član povjerenstva)
Committee memberMirko Sužnjević (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2018-07-06, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Cilj ovog rada bilo je istraživanje i opisivanje mehanizama na kojima radi usluga YouTube za prikazivanje 360 stupnjeva videa, provođenje mjerenja u laboratorijskom testnom prostoru za prikupljanje tragova mrežnog prometa i pokazatelja uspješnosti aplikacijskog sloja. Također smo identificirali skup značajki prometa koji su izvučeni iz tragova mrežnog prometa i testirali ih da bismo ih mogli koristiti za procjenu performansi aplikacijskog sloja pomoću strojnog učenja. Skup podataka koji je analiziran sastoji se od 348 primjeraka. Koristili smo alat za strojno učenje pod nazivom WEKA. Korištenjem WEKA-e iz baze podataka smo izvukli relevantne atribute koji bi mogli imati utjecaja prilikom klasifikacije određenog modela. Napravili smo klasifikacijske modele s 4 različita algoritma strojnog učenja: J48, OneR, Naïve Bayse i SMO. Analizirani su dobiveni rezultati klasifikacije i njihova točnost. Najmanja točnost klasifikacije bila je za početno kašnjenje, a najbolja je bila za rezoluciju u slučaju kad instance dijelimo u dvije klase. Nije bilo značajne razlike u učinkovitosti između izabranih algoritama strojnog učenja.
Abstract (english)
In this thesis the focus was on investigating and describing the mechanisms behind the YouTube 360-degree video service, conducting measurements in a laboratory testbed to collect network traffic traces and application-layer performance indicators. We also identified a set of traffic features that was extracted from network traffic traces and tested them to see can they be used to estimate application-layer performance using a machine learning approach. The dataset that was analysed consists of 348 instances. We used the machine learning tool WEKA. Using WEKA we subset a dataset using wrapper methods and trained classification models with 4 different machine learning algorithms: J48, OneR, Naïve Bayse and SMO. Classification accuracy was assessed and discussed. The worst classification accuracy was for target variable initial delay and best one was for resolution that separate instances in two classes. There was no significant difference in efficiency between algorithms.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:252652
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2019-03-14 01:09:51