Title :
When YouTube Does not Work—Analysis of QoE-Relevant Degradation in Google CDN Traffic
Author :
Casas, Pedro ; D´Alconzo, Alessandro ; Fiadino, Pierdomenico ; Bar, Arian ; Finamore, Alessandro ; Zseby, Tanja
Author_Institution :
Telecommun. Res. Center Vienna, Forschungszentrum Telekommunikation Wien, Vienna, Austria
Abstract :
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, both to improve their watching experience as well as to reduce the load on the core of the network, using dynamic server selection strategies. However, we show that such a dynamic approach can actually have negative effects on the end-user quality of experience (QoE). Through the comprehensive analysis of one month of YouTube flow traces collected at the network of a large European ISP, we report a real case study in which YouTube QoE-relevant degradation affecting a large number of users occurs as a result of Google´s server selection strategies. We present an iterative and structured process to detect, characterize, and diagnose QoE-relevant anomalies in CDN distributed services such as YouTube. The overall process uses statistical analysis methodologies to unveil the root causes behind automatically detected problems linked to the dynamics of CDNs´ server selection strategies.
Keywords :
client-server systems; quality of experience; social networking (online); statistical analysis; telecommunication traffic; CDN distributed services; CDN server selection strategies; European ISP; Google CDN traffic; Google server selection strategies; QoE-relevant anomaly characterization; QoE-relevant anomaly detection; QoE-relevant anomaly diagnosis; QoE-relevant degradation; YouTube QoE-relevant degradation; YouTube flow trace collection; YouTube videos; content delivery network; dynamic approach; dynamic server selection strategies; end-user QoE; end-user quality of experience; iterative structured process; load reduction; statistical analysis methodologies; watching experience improvement; Degradation; Google; IP networks; Servers; Telecommunication traffic; Videos; YouTube; Content Delivery Networks; Quality of Experience; Statistical Data Analysis; Traffic Monitoring; YouTube; content delivery networks; quality of experience; statistical data analysis; traffic monitoring;
Journal_Title :
Network and Service Management, IEEE Transactions on
DOI :
10.1109/TNSM.2014.2377691