@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 = {

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.

}, 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 {grimaudoTNSM2014, title = {SeLeCT: Self-Learning Classifier for Internet Traffic}, journal = {IEEE Transactions on Network and Service Management}, volume = {11}, year = {2014}, month = {06/2014}, chapter = {144}, abstract = {

Network visibility is a critical part of traffic engineering, network management, and security. The most popular\ current solutions - Deep Packet Inspection (DPI) and statistical\ classification, deeply rely on the availability of a training set.\ Besides the cumbersome need to regularly update the signatures,\ their visibility is limited to classes the classifier has been trained\ for. Unsupervised algorithms have been envisioned as a viable\ alternative to automatically identify classes of traffic. However,\ the accuracy achieved so far does not allow to use them for traffic\ classification in practical scenario.

To address the above issues, we propose SeLeCT, a Self-Learning Classifier for Internet Traffic. It uses unsupervised algorithms along with an adaptive seeding approach to automatically\ let classes of traffic emerge, being identified and labeled. Unlike\ traditional classifiers, it requires neither a-priori knowledge of\ signatures nor a training set to extract the signatures. Instead,\ SeLeCT automatically groups flows into pure (or homogeneous)\ clusters using simple statistical features. SeLeCT simplifies label\ assignment (which is still based on some manual intervention) so\ that proper class labels can be easily discovered. Furthermore,\ SeLeCT uses an iterative seeding approach to boost its ability to\ cope with new protocols and applications.

We evaluate the performance of SeLeCT using traffic traces\ collected in different years from various ISPs located in 3\ different continents. Our experiments show that SeLeCT achieves\ excellent precision and recall, with overall accuracy close to 98\%.\ Unlike state-of-art classifiers, the biggest advantage of SeLeCT\ is its ability to discover new protocols and applications in an\ almost automated fashion.

}, keywords = {clustering, self-seeding, Traffic Classification, unsupervised machine learning}, author = {Luigi Grimaudo and Marco Mellia and Elena Baralis and Ram Keralapura} }