Procjena performansi usluga strujanja igara uživo na osnovu analize kriptiranog prometa uporabom metoda strojnog učenja

undergraduate thesis

• Title (english): A Machine Learning Approach for Performance Estimation of Live Game Streaming Based on the Analysis of Encrypted Network Traffic
• Author: Blaž Planinić
• Mentor: Lea Skorin-Kapov (mentor)
• Committee member: Lea Skorin-Kapov (predsjednik povjerenstva)
• Committee member: Ivan Slivar (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

Strujanje video igara uživo putem platforme Twitch.tv jedna je od
najpopularnijih aktivnosti na Internetu danas. Stoga je bitno da ta aktivnost i usluga
budu što bolji. Često se zna dogoditi da te usluge imaju problema, odnosno da je
iskustvena kvaliteta korisnika koji gleda video zapis uživo loša. Ti podaci koji utječu
na iskustvenu kvalitetu su u mrežnom prometu kripitirani, te ne postoji mogućnost
njihovog pregleda u mreži, već samo na aplikacijskoj razini. Stoga, u ovom radu je
u laboratorijskom okruženju ispitano do koje je mjere podatke s aplikacijske razine
moguće procijeniti na temelju podataka s mrežne razine pomoću metoda strojnog
učenja. Dobiveni rezultati govore da je podatke s aplikacijske razine moguće
procijeniti u relativno velikoj točnosti pomoću određenih mrežnih atributa
generiranjem modela strojnog učenja.

Abstract (english)

Nowadays, watching live video gaming using the Twitch.tv platform is one of
the most popular activities on the Internet. For that reason, it is important that
services on this platform are delivered on a high professional level. Often these
services experience difficulties. In other words, the Quality of Experience (QoE) of
the user watching live video can be low. Since traffic is encrypted, it is not possible
to directly detect problems related to KPIs by inspecting application-level packet
headers. The aim of this study was to investigate whether it is possible to estimate
KPIs only from analyzing encrypted traffic using machine learning techniques. As a
result of the analysis, classification accuracy was found to be about 70%.

• Keywords: Twitch.tv; iskustvena kvaliteta; strujanje igara uživo; strojno učenje
• Keywords (english): Twitch.tv; Quality of Experience; machine learning; live video gaming
• Language: croatian
• URN:NBN: urn:nbn:hr:168:375012
• 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: 2019-03-14 00:01:29