@inproceedings {Cas2015a,
	title = {On the Analysis of QoE in Cellular Networks: from Subjective Tests to Large-scale Traffic Measurements},
	booktitle = {6th International Workshop on Traffic Analysis and Characterization (TRAC)},
	year = {2015},
	author = {Pedro Casas and Martin Varela and Pierdomenico Fiadino and Mirko Schiavone and Helena Rivas and Raimund Schatz}
}
@inproceedings {DR:AIMS-15,
	title = {Anycast census and geolocation},
	booktitle = {7th Workshop on Active Internet Measurements (AIMS 2015)},
	year = {2015},
	month = {April 2015},
	url = {http://www.enst.fr/~drossi/paper/rossi15aims.pdf},
	author = {Cicalese, Danilo and Auge, Jordan and Joumblatt, Diana and Rossi, Dario and Friedman, Timur}
}
@inproceedings {E.2014,
	title = {Active measurements and limitations of TCP protocol during SLA test},
	booktitle = {Poster session the Sixth Workshop on Traffic and Monitoring Analysis (TMA)},
	year = {2014},
	month = {04/2014},
	author = {Edion Tego}
}
@inbook {Sil2014,
	title = {Aggregation of Statistical Data from Passive Probes: Techniques and Best Practices},
	booktitle = {Traffic Monitoring and Analysis},
	series = {Lecture Notes in Computer Science},
	volume = {8406},
	year = {2014},
	pages = {38-50},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	abstract = {<p>Passive probes continuously generate statistics on large number of metrics, that are possibly represented as probability mass functions (pmf). The need for consolidation of several pmfs arises in two contexts, namely: (i) whenever a central point collects and aggregates measurement of multiple disjoint vantage points, and (ii) whenever a local measurement processed at a single vantage point needs to be distributed over multiple cores of the same physical probe, in order to cope with growing link capacity. In this work, we take an experimental approach and study both cases using, whenever possible, open source software and datasets. Considering different consolidation strategies, we assess their accuracy in estimating pmf deciles (from the 10th to the 90th) of diverse metrics, obtaining general design and tuning guidelines. In our dataset, we find that Monotonic Spline Interpolation over a larger set of percentiles (e.g., adding 5th, 10th, 15th, and so on) allow fairly accurate pmf consolidation in both the multiple vantage points (median error is about 1\%, maximum 30\%) and local processes (median 0.1\%, maximum 1\%) cases.</p>},
	keywords = {Data aggregation, data reduction, scalability problem},
	isbn = {978-3-642-54998-4},
	doi = {10.1007/978-3-642-54999-1_4},
	url = {http://dx.doi.org/10.1007/978-3-642-54999-1_4},
	author = {Colabrese, Silvia and Rossi, Dario and Mellia, Marco}
}
@article {Dus2014a,
	title = {Algorithm and Scheduler Design and Implementation},
	year = {2014},
	month = {09/2014},
	keywords = {algorithm design, job scheduler, mPlane software, repository tools},
	isbn = {D3.3},
	issn = {D3.3},
	author = {Maurizio Dusi and Saverio Niccolini and Sofia Nikitaki and Daniele Apiletti and Elena Baralis and Alessandro Finamore and Luigi Grimaudo and Stefano Traverso and Francesco Matera and Edion Tego and V, Guchev and Zied Ben Houidi and Pietro Michiardi and Marco Milanesio and YiXi Gong and Dario Rossi and Ilias Leontiadis and G, Dimopoulos and Tivadar Szemethy and A, Bakay and Arian B{\"a}r and Pedro Casas and Alessandro D{\textquoteright}Alconzo and Pierdomenico Fiadino}
}
@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}
}
@inproceedings {DR:ICN-14c,
	title = {Analyzing Cacheable Traffic in ISP Access Networks for Micro CDN applications via Content-Centric Networking},
	booktitle = {ACM SIGCOMM Information Centric Networks (ICN)},
	year = {2014},
	month = {09/2014},
	address = {Paris, FR},
	url = {http://www.enst.fr/~drossi/paper/rossi14icn-c.pdf},
	author = {Imbrenda, Claudio and Muscariello, Luca and Rossi, Dario}
}
