@inproceedings {Kut2015, title = {Weighted Similarity Estimation in Data Streams}, booktitle = {CIKM}, year = {2015}, month = {10/2015}, publisher = {ACM}, organization = {ACM}, abstract = {

Similarity computation between pairs of objects is often a bottleneck in many applications that have to deal with massive volumes of data. Motivated by applications such as collaborative filtering in large-scale recommender systems, and influence probabilities learning in social networks, we present new randomized algorithms for the estimation of weighted similarity in data streams.

Previous works have addressed the problem of learning binary similarity measures in a streaming setting. To the best of our knowledge, the algorithms proposed here are the first that specifically address the estimation of weighted similarity in data streams. The algorithms need only one pass over the data, making them ideally suited to handling massive data streams in real time.

We obtain precise theoretical bounds on the approximation error and complexity of the algorithms. The results of evaluating our algorithms on two real-life datasets validate the theoretical findings and demonstrate the applicability of the proposed algorithms.

}, author = {Konstantin Kutzkov and Mohamed Ahmed and Sofia Nikitaki} } @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} } @inproceedings {A2014, title = {Who to Blame when YouTube is not Working? Detecting Anomalies in CDN Provisioned Services}, booktitle = {TRAC}, year = {2014}, month = {08/2014}, publisher = {IEEE}, organization = {IEEE}, address = {Nicosia, Cyprus}, author = {Alessandro D{\textquoteright}Alconzo and Pedro Casas and Pierdomenico Fiadino and Arian B{\"a}r and Alessandro Finamore} } @inproceedings {Rio2012, title = {Wire-speed statistical classification of network traffic on commodity hardware}, booktitle = {ACM Internet Measurement Conference (IMC)}, year = {2012}, month = {11/2012}, abstract = {

In this paper we present a software-based traffic classification engine running on commodity multi-core hardware, able to process in real-time aggregates of up to 14.2Mpps over a single 10Gbps interface -- i.e., the maximum possible packet rate over a 10Gbps Ethernet links given the minimum frame size of 64Bytes. This significant advance with respect to the current state of the art in terms of achieved classification rates are made possible by: (i) the use of an improved network driver, PacketShader, to efficiently move batches of packets from the NIC to the main CPU; (ii) the use of lightweight statistical classification techniques exploiting the size of the first few packets of every observed flow; (iii) a careful tuning of critical parameters of the hardware environment and the software application itself.

}, doi = {10.1145/2398776.2398784}, author = {Pedro Maria Santiago del Rio and D Rossi and Francesco Gringoli and Lorenzo Nava and Luca Salgarelli and Javier Aracil} }