@inproceedings {Hou2015, title = {A Study of the Impact of DNS Resolvers on Performance Using a Causal Approach}, booktitle = {Internet Teletraffic Congress}, year = {2015}, month = {08/2015}, address = {Ghent, Belgium}, abstract = {For a user to access any resource on the Internet, it is necessary to first locate a server hosting the requested resource. The Domain Name System service (DNS) represents the first step in this process, translating a human readable name, the resource host name, into an IP address. With the expansion of Content Distribution Networks (CDNs), the DNS service has seen its importance increase. In a CDN, objects are replicated on different servers to decrease the distance from the client to a server hosting the object that needs to be accessed. The DNS service should improve user experience by directing its demand to the optimal CDN server. While most of the Internet Service Providers (ISPs) offer a DNS service to their customers, it is now common to see clients using a public DNS service instead. This choice may have an impact on Web browsing performance. In this paper we study the impact of choosing one DNS server instead of another and we compare the performance of a large European ISP DNS service with the one of a public DNS service, Google DNS. We propose a causal approach to expose the structural dependencies of the different parameters impacted by the DNS service used and we show how to model these dependencies with a Bayesian network. This model allows us to explain and quantify the benefits obtained by clients using their ISP DNS service and to propose a solution to further improve their performance.}, keywords = {DNS, reasoner}, author = {Hadrien Hours and Ernst Biersack and Patrick Loiseau and Alessandro Finamore and Marco Mellia} } @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 = {

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} } @inproceedings {Nay2014, title = {The Cost of the {\textquotedblleft}S{\textquotedblright} in HTTPS}, booktitle = {ACM Conference on emerging Networking EXperiments and Technologies (CoNEXT)}, year = {2014}, month = {12/2014}, author = {David Naylor and Alessandro Finamore and Ilias Leontiadis and Yan Grunenberger and Marco Mellia and Kostantina Papagiannaki and Peter Steenkiste} } @inproceedings {Bae2014, title = {DBStream: an Online Aggregation, Filtering and Processing System for Network Traffic Monitoring}, booktitle = {TRAC}, year = {2014}, month = {08/2014}, publisher = {IEEE}, organization = {IEEE}, address = {Nicosia, Cyprus}, author = {Arian Baer and Pedro Casas and Lukasz Golab and Alessandro Finamore} } @inproceedings {Fia2014, title = {On the Detection of Network Traffic Anomalies in Content Delivery Network Services}, booktitle = {ITC26}, year = {2014}, month = {09/2014}, address = {Karlskrona, Sweden}, author = {Pierdomenico Fiadino and Alessandro D{\textquoteright}Alconzo and Arian B{\"a}r and Alessandro Finamore and Pedro Casas} } @inproceedings {Ben2014, title = {Gold mining in a River of Internet Content Traffic}, booktitle = {6th International Workshop on Traffic Monitoring and Analysis, TMA}, year = {2014}, month = {04/2014}, publisher = {Springer}, organization = {Springer}, address = {London}, abstract = {With the advent of Over-The-Top content providers (OTTs), Internet Service Providers (ISPs) saw their portfolio of services shrink to the low margin role of data transporters. In order to counter this effect, some ISPs started to follow big OTTs like Facebook and Google in trying to turn their data into a valuable asset. In this paper, we explore the questions of what meaningful information can be extracted from network data, and what interesting insights it can provide. To this end, we tackle the first challenge of detecting {\textquotedblleft}user-URLs{\textquotedblright}, i.e., those links that were clicked by users as opposed to those objects automatically downloaded by browsers and applications. We devise algorithms to pinpoint such URLs, and validate them on manually collected ground truth traces. We then apply them on a three-day long traffic trace spanning more than 19,000 residential users that generated around 190 million HTTP transactions. We find that only 1.6\% of these observed URLs were actually clicked by users. As a first application for our methods, we answer the question of which platforms participate most in promoting the Internet content. Surprisingly, we find that, despite its notoriety, only 11\% of the user URL visits are coming from Google Search. }, keywords = {Content mining, HTTP Traffic, URL extraction}, author = {Zied Ben-Houidi and Giuseppe Scavo and Samir Ghamri-Doudane and Alessandro Finamore and Stefano Traverso and Marco Mellia} } @inproceedings {Bae2014a, title = {Large-Scale Network Traffic Monitoring with DBStream, a System for Rolling Big Data Analysis}, booktitle = {International Conference on Big Data, IEEE BigData}, year = {2014}, month = {11/2014}, publisher = {IEEE}, organization = {IEEE}, address = {Washington D.C., USA}, abstract = {

The complexity of the Internet has rapidly increased, making it more important and challenging to design scalable network monitoring tools. Network monitoring typically requires rolling data analysis, i.e., continuously and incrementally updating (rolling-over) various reports and statistics over high-volume data streams. In this paper, we describe DBStream, which is an SQL-based system that explicitly supports incremental queries for rolling data analysis. We also present a performance comparison of DBStream with a parallel data processing engine (Spark), showing that, in some scenarios, a single DBStream node can outperform a cluster of ten Spark nodes on rolling network monitoring workloads. Although our performance evaluation is based on network monitoring data, our results can be generalized to other big data problems with high volume and velocity.

}, keywords = {Big Data Analysis, Data Stream Processing, network data analysis, System Performance}, author = {Arian B{\"a}r and Alessandro Finamore and Pedro Casas and Lukasz Golab and Marco Mellia} } @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 {Cas2014b, title = {YouTube All Around: Characterizing YouTube from Mobile and Fixed-line Network Vantage Points}, booktitle = {EuCNC}, year = {2014}, month = {06/2014}, address = {Bologna, IT}, author = {Pedro Casas and Pierdomenico Fiadino and Arian B{\"a}r and Alessandro D{\textquoteright}Alconzo and Alessandro Finamore and Marco Mellia} } @article {Mic2013, title = {Basic Network Data Analysis}, number = {D3.1}, year = {2013}, month = {05/2013}, institution = {mPlane Consortium}, type = {Public Deliverable}, address = {Torino}, abstract = {

This document describes the requirements, input, output for the algorithms needed to perform analytic tasks on a large amount of data, in the context of WP3. Starting from the use cases defined in WP1, we identify the algorithms needed to address the various scenario requirements. Operating on a large amount of data, these algorithms strive for parallel and scalable approaches; the designing and implementation of the algorithm itself can be a challenging research task since today very little is known concerning how to develop efficient and scalable algorithms that runs on parallel processing frameworks.
The algorithm in the storage layer are characterized by the fact that they operate on a large amount of data, and produce a concise representation of it, extracting features and aggregating it, so that the produced output is easier to handle and understand. Depending on the amount of data produced, on the scenario characteristics and on the time constraints, algorithms can require a real time (or near real time) or a batch processing.
For each algorithm and use case, the input data and the initial state, the computation to run and the output produced are described.

}, keywords = {algorithms, big data, storage}, issn = {D3.1}, author = {Pietro Michiardi and Antonio Barbuzzi and Alessandro Finamore and Stefano Traverso and Daniele Apiletti and Elena Baralis and Tania Cerquitelli and Silvia Chiusano and Luigi Grimaudo and A. Rufini and Francesco Matera and A. Valentii and Maurizio Dusi and Mohamed Ahmed and Tivadar Szemethy and L. N{\'e}meth and R. Szalay and Ilias Leontiadis and Yan Grunenberger and P. Casas and Alessandro D{\textquoteright}Alconzo and A B{\"a}r and D Rossi and YiXi Gong} } @article {Bar2013a, title = {Database Layer Design}, number = {D3.2}, year = {2013}, month = {11/2013}, institution = {mPlane Consortium}, type = {Public Deliverable}, address = {Torino}, keywords = {big data, databases, repositories, storage}, issn = {D3.2}, author = {A B{\"a}r and P. Casas and Alessandro D{\textquoteright}Alconzo and Alessandro Finamore and Antonio Barbuzzi and Maurizio Dusi and Gianni De Rosa and Tivadar Szemethy and Ilias Leontiadis and D Rossi and Jordan Aug{\'e} and Marc-Oliver Buob} } @article {Pap2013, title = {Design of Analysis Modules}, number = {D4.1}, year = {2013}, month = {11/2013}, institution = {mPlane Consortium}, type = {Public Deliverable}, address = {Torino}, keywords = {algorithms, analysis}, isbn = {D4.1}, issn = {D4.1}, author = {Dimitri Papadimitriou and Zied Ben-Houidi and Samir Ghamri-Doudane and D Rossi and M. Milanesio and P. Casas and Alessandro D{\textquoteright}Alconzo and Edion Tego and Francesco Matera and Maurizio Dusi and Tivadar Szemethy and L. M{\'a}th{\'e} and Alessandro Finamore and Stefano Traverso and Ilias Leontiadis and Yan Grunenberger and L. Baltrunas and Benoit Donnet and Guy Leduc and Y. Liao} } @article {Cap2013, title = {First Data Collection Track Record}, number = {D5.1}, year = {2013}, month = {11/2013}, institution = {mPlane Consortium}, type = {Private Deliverable}, address = {Torino}, keywords = {data sets, integration, measurement systems, scenarios, use cases}, author = {Alessandro Capello and Fabrizio Invernizzi and Omar Jabr and Dimitri Papadimitriou and Dario Rossi and YiXi Gong and Brian Trammell and Marco Milanesio and Ernst Biersack and Rolf Winter and Francesco Matera and Arianna Rufini and Edion Tego and Maurizio Dusi and Balazs Szabo and Tivadar Szemethy and Alessandro Finamore and Marco Mellia and Ilias Leontiadis and Benoit Donnet} } @inproceedings {Ros2013, title = {I tube, YouTube, P2PTube: assessing ISP benefits of peer-assisted caching of YouTube content}, booktitle = {IEEE P2P{\textquoteright}XIII}, year = {2013}, month = {09/2013}, abstract = {

This paper proposes P2PTube, a very simple yet effective set-top-box system to assist diffusion of YouTube videos. We argue that, due to the spatial and temporal nature of video requests, the simplest design already provides non marginal gains. Our trace driven evaluation shows that, with moderate cache size (100MB) and nominal upload rates (500Kbps), about half of the video requests could be served by P2PTube. Interestingly, we also see that non marginal gains are already achievable with tiny caches {\textendash} which is tied to the presence of advertisement prior that the actual video requested by the user.

}, url = {http://www.enst.fr/~drossi/paper/rossi13p2p-b.pdf}, author = {D Rossi and Y. Nicolas and D. Wolff and Alessandro Finamore} } @inproceedings {Tra2013c, title = {A Measurement-Centered Approach to Latency Reduction}, booktitle = {ISOC Workshop on Reducing Internet Latency}, year = {2013}, month = {09/2013}, type = {Position Paper}, address = {London, England}, url = {http://www.internetsociety.org/latency2013}, author = {Brian Trammell and Alessandro Finamore and Marco Mellia} } @article {Tra2013d, title = {mPlane Architecture Specification}, number = {D1.3}, year = {2013}, month = {11/2013}, institution = {mPlane Consortium}, type = {Public Deliverable}, address = {Torino}, keywords = {architecture, measurement, platform, scenario, use case}, author = {Brian Trammell and Marco Mellia and Alessandro Finamore and Stefano Traverso and Tivadar Szemethy and Balazs Szabo and D Rossi and Benoit Donnet and Fabrizio Invernizzi and Dimitri Papadimitriou} } @article {Dus2013, title = {Plans for Using and Disseminating mPlane Knowledge}, number = {D7.2}, year = {2013}, month = {11/2013}, institution = {mPlane Consortium}, type = {Public Deliverable}, address = {Torino}, keywords = {dissemination, exploitation, open-source software, publications, standardization}, issn = {D7.2}, author = {Maurizio Dusi and Saverio Niccolini and Antonio Barbuzzi and M. Milanesio and Alessandro Finamore and Brian Trammell and Edion Tego and Francesco Matera and Tivadar Szemethy and Rolf Winter and Zied Ben-Houidi and Andrea Fregosi and Benoit Donnet and Fabrizio Invernizzi and Dimitri Papadimitriou and Ilias Leontiadis and Yan Grunenberger and P. Casas} } @article {Pap2013c, title = {Selection of Existing Probes and Datasets}, number = {D2.1}, year = {2013}, month = {08/2013}, institution = {mPlane Consortium}, type = {Public Deliverable}, address = {Torino}, abstract = {The mPlane architecture has been designed to include the possibility to interface with existing systems and platforms. While most measurement platforms in existence target a very specific measurement use case (e.g., the discovery of the Internet{\textquoteright}s router-level topology, the continuous measurement of the RTT among host pairs, the exporting via SNMP of network state, etc.), there are platforms that have a large deployed base, with lot of data being at disposal, and/or continuously collecting data. It would be a waste of resources to merely reproduce this effort within mPlane. Instead, mPlane aims at directly interfacing with existing systems and re-using their capabilities and data to feed measurement results to the mPlane intelligence. This document lists selected existing systems that are important for mPlane either for theoretical, conceptual or practical reasons, and that are part of the background of mPlane partners. A sub-set of these systems will be eventually incorporated into mPlane by developing the necessary interfaces. Others could be integrated by the means of proxy probes, i.e., the conceptual component responsible for such interfacing. The main focus of this document is to elaborate the concept of proxy probes, enumerate the systems that will be possibly considered for interface (proxy probe) development, and to give high level descriptions of the proxy probe design for these systems. The following list enumerates the systems that the consortium has chosen to include: - QoF - a TCP-aware IPFIX flow meter  Cisco Ping and SLA Agents - commercial availability and basic network parameter agents  - Tracebox - a tool for middlebox detection and identification - Scamper - a sophisticated active probing tool - MERLIN - a router-level topology discovery tool - TopHat - a configurable measurement system on top of PlanetLab - Tstat - a passive network monitoring tool - BlockMon - a flexible network monitoring and analysis tool - MisuraInternet - a QoS measurement system - Firelog - a Firefox plugin to measure HTTP QoE - Pytomo - an end-host-based video OoE measurement tool - DATI - a high performance deep packet inspector - MobiPerf - a tool for monitoring smartphone performance}, keywords = {active probes, existing probes, passive probes, probes, proxy probes}, author = {Dimitri Papadimitriou and Dario Rossi and YiXi Gong and Brian Trammell and Marco Milanesio and Ernst Biersack and Rolf Winter and Francesco Matera and Maurizio Dusi and Balazs Szabo and Tivadar Szemethy and Alessandro Finamore and Marco Mellia and Alessandro Capello and Fabio Invernizzi and Omar Jabr and Ilias Leontiadis and Benoit Donnet} } @inproceedings {Val2012, title = {Breaking for commercials: characterizing mobile advertising}, booktitle = {Proceedings of the 2012 ACM conference on Internet measurement conference}, year = {2012}, publisher = {ACM}, organization = {ACM}, address = {Boston, MA}, abstract = {

Mobile phones and tablets can be considered as the first incarnation of the post-PC era. Their explosive adoption rate has been driven by a number of factors, with the most signifcant influence being applications (apps) and app markets. Individuals and organizations are able to develop and publish apps, and the most popular form of monetization is mobile advertising.

The mobile advertisement (ad) ecosystem has been the target of prior research, but these works typically focused on a small set of apps or are from a user privacy perspective. In this work we make use of a unique, anonymized data set corresponding to one day of traffic for a major European mobile carrier with more than 3 million subscribers. We further take a principled approach to characterize mobile ad traffic along a number of dimensions, such as overall traffic, frequency, as well as possible implications in terms of en- ergy on a mobile device.

Our analysis demonstrates a number of inefficiencies in today{\textquoteright}s ad delivery. We discuss the benefits of well-known techniques, such as pre-fetching and caching, to limit the energy and network signalling overhead caused by current systems. A prototype im- plementation on Android devices demonstrates an improvement of 50\% in terms of energy consumption for offline ad-sponsored apps while limiting the amount of ad related traffic.\ 

}, keywords = {advertisement, caching, cellular, energy, smartphones, traffic}, isbn = {978-1-4503-1705-4}, doi = {10.1145/2398776.2398812}, url = {http://doi.acm.org/10.1145/2398776.2398812}, author = {Narseo Vallina-Rodriguez and Jay Shah and Alessandro Finamore and Yan Grunenberger and Konstantina Papagiannaki and Hamed Haddadi and Jon Crowcroft} } @inproceedings {Fin2012, title = {The need for an intelligent measurement plane: The example of time-variant CDN policies}, booktitle = {Telecommunications Network Strategy and Planning Symposium (NETWORKS), 2012 XVth International }, year = {2012}, month = {10/2012}, pages = {1 - 6 }, abstract = {In this paper we characterize how web-based services are delivered by large organizations in today{\textquoteright}s Internet. Taking advantage oftwo week-long data sets separated in time by 10 months and reporting the web activity of more than 10,000 ADSL residential customers, we identify the services offered by large organizations like Google, Akamai and Amazon. We then compare theevolution of both policies used to serve requests, and the infrastructure they use to match the users{\textquoteright} demand. Results depict anovercrowded scenario in constant evolution. Big-players are more and more responsible for the majority of the volume and a plethora of other organizations offering similar or more specific services through different CDNs and traffic policies. Unfortunately, no standard tools and methodologies are available to capture and expose the hidden properties of this in constant evolution picture. A deeper understanding of such dynamics is however fundamental to improve the performance of current and future Internet. To this extend, we claim the need for a Internet-wide, standard, flexible and intelligent measurement plane to be added tothe current Internet infrastructure.}, keywords = {Facebook, Google, Monitoring, Organizations, Servers, Streaming media, Throughput}, issn = {978-1-4673-1390-2}, doi = {http://dx.doi.org/10.1109/NETWKS.2012.6381662}, author = {Alessandro Finamore and Vinicius Gehlen and Marco Mellia and Maurizio M Munafo{\textquoteright}} }