@inproceedings {Pie2015,
	title = {Vivisecting WhatsApp in Cellular Networks: Servers, Flows, and Quality of Experience},
	booktitle = {Traffic Monitoring and Analysis},
	year = {2015},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	keywords = {Cellular Networks, Large-Scale Measurements, Quality of Experience, Traffic characterization, WhatsApp},
	isbn = {978-3-319-17171-5},
	doi = {10.1007/978-3-319-17172-2_4},
	url = {http://dx.doi.org/10.1007/978-3-319-17172-2_4},
	author = {Fiadino, Pierdomenico and Schiavone, Mirko and Casas, Pedro}
}
@inproceedings {Cas2014d,
	title = {On the Analysis of QoE-based Performance Degradation in YouTube Traffic},
	booktitle = {10th International Conference on Network and Service Management, CNSM 2014},
	year = {2014},
	month = {11/2014},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Rio de Janeiro, Brazil},
	abstract = {<p>YouTube is the most popular service in today{\textquoteright}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{\textquoteright} 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{\textquoteright}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{\textquoteright}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.</p>},
	keywords = {clustering, Content Delivery Networks, Empirical Entropy, Performance Degradation, Quality of Experience, YouTube},
	author = {Pedro Casas and Alessandro D{\textquoteright}Alconzo and Pierdomenico Fiadino and Arian B{\"a}r and Alessandro Finamore}
}
@article {6975242,
	title = {When YouTube Does not Work - Analysis of QoE-Relevant Degradation in Google CDN Traffic},
	journal = {Network and Service Management, IEEE Transactions on},
	volume = {11},
	year = {2014},
	month = {Dec},
	pages = {441-457},
	keywords = {CDN distributed services, CDN server selection strategies, client-server systems, content delivery network, Content Delivery Networks, Degradation, dynamic approach, dynamic server selection strategies, end-user QoE, end-user quality of experience, European ISP, Google, Google CDN traffic, Google server selection strategies, IP networks, iterative structured process, load reduction, QoE-relevant anomaly characterization, QoE-relevant anomaly detection, QoE-relevant anomaly diagnosis, QoE-relevant degradation, Quality of Experience, Servers, social networking (online), statistical analysis, statistical analysis methodologies, Statistical Data Analysis, telecommunication traffic, Traffic Monitoring, Videos, watching experience improvement, YouTube, YouTube flow trace collection, YouTube QoE-relevant degradation, YouTube videos},
	issn = {1932-4537},
	doi = {10.1109/TNSM.2014.2377691},
	author = {Casas, Pedro and D{\textquoteright}Alconzo, Alessandro and Fiadino, Pierdomenico and B{\"a}r, Arian and Finamore, Alessandro and Zseby, Tanja}
}
