<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pedro Casas</style></author><author><style face="normal" font="default" size="100%">Alessandro D'Alconzo</style></author><author><style face="normal" font="default" size="100%">Pierdomenico Fiadino</style></author><author><style face="normal" font="default" size="100%">Arian Bär</style></author><author><style face="normal" font="default" size="100%">Alessandro Finamore</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On the Analysis of QoE-based Performance Degradation in YouTube Traffic</style></title><secondary-title><style face="normal" font="default" size="100%">10th International Conference on Network and Service Management, CNSM 2014</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">Content Delivery Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Empirical Entropy</style></keyword><keyword><style  face="normal" font="default" size="100%">Performance Degradation</style></keyword><keyword><style  face="normal" font="default" size="100%">Quality of Experience</style></keyword><keyword><style  face="normal" font="default" size="100%">YouTube</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2014</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Rio de Janeiro, Brazil</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;YouTube is the most popular service in today's Internet. Google relies on its massive Content Delivery Network (CDN) to push YouTube videos as close as possible to the end-users to improve their Quality of Experience (QoE), as well as to pursue its own optimization goals. Adopting space and time variant traffic delivery policies, Google servers handle users' requests from multiple geo-distributed locations at different times. Such traffic delivery policies can have a relevant impact on the traffic routed through the Internet Service Providers (ISPs) providing the access, but most importantly, they can have negative effects on the end-user QoE. In this paper we shed light on the problem of diagnosing QoE-based performance degradation events in YouTube's traffic. Through the analysis of one month of YouTube flow traces collected at the network of a large European ISP, we particularly identify and drill down a Google's CDN server selection policy negatively impacting the watching experience of YouTube users during several days at peak-load times. The analysis combines both the user-side perspective and the CDN perspective of the end-to-end YouTube delivery service to diagnose the problem. On the one hand, we rely on the monitoring of YouTube QoE-based Key Performance Indicators (KPIs) to detect performance degradation events affecting the end-customers. On the other hand, we analyze the temporal behavior of the Google CDN traffic delivery policies, by tracking the activity of the Google servers providing the videos. The analysis is supported by time-series analysis, entropy-based approaches, and clustering techniques to flag the aforementioned anomaly. The main contributions of the paper are threefold: firstly, we provide a large-scale characterization of the YouTube service in terms of traffic characteristics and provisioning behavior of the Google CDN servers. Secondly, we introduce simple yet effective QoE-based KPIs to monitor YouTube videos from the end-user perspective. Finally and most important, we analyze and provide evidence of the occurrence of QoE-based YouTube anomalies induced by CDN server selection policies, which are somehow normally hidden from the common knowledge of the end-user. This is a main issue for ISPs, who see their reputation degrade when such events occur, even if Google is the culprit.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Casas, Pedro</style></author><author><style face="normal" font="default" size="100%">D'Alconzo, Alessandro</style></author><author><style face="normal" font="default" size="100%">Fiadino, Pierdomenico</style></author><author><style face="normal" font="default" size="100%">Bär, Arian</style></author><author><style face="normal" font="default" size="100%">Finamore, Alessandro</style></author><author><style face="normal" font="default" size="100%">Zseby, Tanja</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">When YouTube Does not Work - Analysis of QoE-Relevant Degradation in Google CDN Traffic</style></title><secondary-title><style face="normal" font="default" size="100%">Network and Service Management, IEEE Transactions on</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CDN distributed services</style></keyword><keyword><style  face="normal" font="default" size="100%">CDN server selection strategies</style></keyword><keyword><style  face="normal" font="default" size="100%">client-server systems</style></keyword><keyword><style  face="normal" font="default" size="100%">content delivery network</style></keyword><keyword><style  face="normal" font="default" size="100%">Content Delivery Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Degradation</style></keyword><keyword><style  face="normal" font="default" size="100%">dynamic approach</style></keyword><keyword><style  face="normal" font="default" size="100%">dynamic server selection strategies</style></keyword><keyword><style  face="normal" font="default" size="100%">end-user QoE</style></keyword><keyword><style  face="normal" font="default" size="100%">end-user quality of experience</style></keyword><keyword><style  face="normal" font="default" size="100%">European ISP</style></keyword><keyword><style  face="normal" font="default" size="100%">Google</style></keyword><keyword><style  face="normal" font="default" size="100%">Google CDN traffic</style></keyword><keyword><style  face="normal" font="default" size="100%">Google server selection strategies</style></keyword><keyword><style  face="normal" font="default" size="100%">IP networks</style></keyword><keyword><style  face="normal" font="default" size="100%">iterative structured process</style></keyword><keyword><style  face="normal" font="default" size="100%">load reduction</style></keyword><keyword><style  face="normal" font="default" size="100%">QoE-relevant anomaly characterization</style></keyword><keyword><style  face="normal" font="default" size="100%">QoE-relevant anomaly detection</style></keyword><keyword><style  face="normal" font="default" size="100%">QoE-relevant anomaly diagnosis</style></keyword><keyword><style  face="normal" font="default" size="100%">QoE-relevant degradation</style></keyword><keyword><style  face="normal" font="default" size="100%">Quality of Experience</style></keyword><keyword><style  face="normal" font="default" size="100%">Servers</style></keyword><keyword><style  face="normal" font="default" size="100%">social networking (online)</style></keyword><keyword><style  face="normal" font="default" size="100%">statistical analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">statistical analysis methodologies</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical Data Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">telecommunication traffic</style></keyword><keyword><style  face="normal" font="default" size="100%">Traffic Monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">Videos</style></keyword><keyword><style  face="normal" font="default" size="100%">watching experience improvement</style></keyword><keyword><style  face="normal" font="default" size="100%">YouTube</style></keyword><keyword><style  face="normal" font="default" size="100%">YouTube flow trace collection</style></keyword><keyword><style  face="normal" font="default" size="100%">YouTube QoE-relevant degradation</style></keyword><keyword><style  face="normal" font="default" size="100%">YouTube videos</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Dec</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">441-457</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pedro Casas</style></author><author><style face="normal" font="default" size="100%">Pierdomenico Fiadino</style></author><author><style face="normal" font="default" size="100%">Andreas Sackl</style></author><author><style face="normal" font="default" size="100%">Alessandro D'Alconzo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">YouTube in the Move: Understanding the Performance of YouTube in Cellular Networks (BEST PAPER AWARD RUNNER UP)</style></title><secondary-title><style face="normal" font="default" size="100%">Wireless Days 2014</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cellular Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Content Delivery Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">End-device Measurements</style></keyword><keyword><style  face="normal" font="default" size="100%">QoE</style></keyword><keyword><style  face="normal" font="default" size="100%">Traffic Measurements</style></keyword><keyword><style  face="normal" font="default" size="100%">YouTube</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2014</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Rio de Janeiro, Brazil</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;YouTube is the most popular and volume-dominant service in today's Internet, and is changing the way ISPs manage their networks. Understanding the performance of YouTube traffic is paramount for ISPs, specially for mobile operators, who must handle the huge surge of traffic with the constraints and challenges of cellular networks. In this paper we present an empirical analysis of the performance of YouTube flows accessed through a national-wide cellular network, considering download throughput as well as end-user Quality of Experience (QoE) metrics. The analysis considers the characteristics and impacts of the Content Delivery Network hosting YouTube, and compares its behavior with other popular HTTP video streaming services accessed through cellular networks. The QoE analysis is performed through end-user device measurements, which directly reflect the experience of the end-users. Our study additionally shows the potentiality of monitoring YouTube performance in cellular networks directly from the smart-phones of the users, bypassing the traffic visibility loss at the core of the network introduced by traffic encryption (e.g., HTTPS).&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">P. Casas</style></author><author><style face="normal" font="default" size="100%">M. Seufert</style></author><author><style face="normal" font="default" size="100%">R. Schatz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">YOUQMON: A System for On-line Monitoring of YouTube QoE in Operational 3G Networks</style></title><secondary-title><style face="normal" font="default" size="100%">31st IFIP Performance</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">3G Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">MOS</style></keyword><keyword><style  face="normal" font="default" size="100%">QoE Monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">Stallings</style></keyword><keyword><style  face="normal" font="default" size="100%">YouTube</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;YouTube is changing the way operators manage network performance monitoring. In this paper we introduce YOUQMON, a novel on-line monitoring system for assessing the Quality of Experience (QoE) undergone by HSPA/3G customers watching YouTube videos, using network-layer measurements only. YOUQMON combines passive traffic analysis techniques to detect stalling events in YouTube video streams, with a QoE model to map stallings into a Mean Opinion Score reflecting the end-user experience. We evaluate the stalling detection performance of YOUQMON with hundreds of YouTube video streams, and present results showing the feasibility of performing real-time YouTube QoE monitoring in an operational mobile broadband network.&lt;/p&gt;</style></abstract></record></records></xml>