@inproceedings {Pap2016a, title = {Mixed-Integer Optimization for the Combined capacitated Facility Location-Routing Problem}, booktitle = {International Conference on the Design of Reliable Communication Networks (DRCN) 2016}, year = {2016}, publisher = {IEEE XPlore}, organization = {IEEE XPlore}, address = {Paris, France}, author = {Dimitri Papadimitriou and Didier Colle and Piet Demeester} } @article {Cas2016, title = {Unveiling Network and Service Performance Degradation in the Wild with mPlane}, journal = {IEEE Communications Magazine - Network Testing Series}, year = {2016}, abstract = {

Unveiling network and service performance issues in complex and highly decentralized systems such as the Internet is a major challenge. Indeed, the Internet is based on decentralization and diversity. However, its distributed nature leads to operational brittleness and difficulty in identifying the root causes of performance degradation. In such a context, network measurements are a fundamental pillar to shed light and to unveil design and implementation defects. To tackle this fragmentation and visibility problem, we have recently conceived mPlane, a distributed measurement platform which runs, collects and analyses traffic measurements to study the operation and functioning of the Internet. In this paper, we show the potentiality of the mPlane approach to unveil network and service degradation issues in live, operational networks, involving both fixed-line and cellular networks. In particular, we combine active and passive measurements to troubleshoot problems in end-customer Internet access connections, or to automatically detect and diagnose anomalies in Internet-scale services (e.g., YouTube) which impact a large number of end-users.

}, author = {Pedro Casas and Pierdomenico Fiadino and Sarah Wassermann and Stefano Traverso and Alessandro D{\textquoteright}Alconzo and Edion Tego and Francesco Matera and Marco Mellia} } @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 {Bae2015, title = {Cache Oblivious Scheduling of Shared Workloads}, booktitle = {31st IEEE International Conference on Data Engineering (ICDE)}, year = {2015}, month = {05/2015}, publisher = {IEEE}, organization = {IEEE}, address = {Seoul, Korea}, abstract = {

Shared workload optimization is feasible if the set of tasks to be executed is known in advance, as is the case in updating a set of materialized views or executing an extract-transform-load workflow. In this paper, we consider dataintensive shared workloads with precedence constraints arising from data dependencies, i.e., before executing some task, other tasks may have to run first and generate some data needed by the next task(s). While there has been previous work on identifying common subexpressions in shared workloads and task re-ordering to enable shared scans, in this paper we go a step further and solve the problem of scheduling shared data-intensive workloads in a cache-oblivious way. Our solution relies on a novel formulation of precedence constrained scheduling with the additional constraint that once a data item is in the cache, all tasks that require this data item should execute as soon as possible thereafter. The intuition behind this formulation is that the longer a data item remains in the cache, the more likely it is to be evicted regardless of the cache size. We give an optimal ordering algorithm using A* search over the space of possible orderings, and we propose efficient and effective heuristics that obtain nearly-optimal results in much less time. We present experimental results on real-life data warehouse workloads and the TCP-DS benchmark to validate our claims.

}, author = {Arian B{\"a}r and Lukasz Golab and Stefan Ruehrup and Mirko Schiavone and Pedro Casas} } @inproceedings {Fia2015b, title = {Challenging Entropy-based Anomaly Detection and Diagnosis in Cellular Networks}, booktitle = {ACM SIGCOMM}, year = {2015}, author = {Pierdomenico Fiadino and Alessandro D{\textquoteright}Alconzo and Mirko Schiavone and Pedro Casas} } @inproceedings {DR:CoNEXT-15, title = {Characterizing IPv4 Anycast Adoption and Deployment}, booktitle = {ACM CoNEXT}, year = {2015}, month = {12/2015}, publisher = {ACM}, organization = {ACM}, address = {Heidelberg, DE}, url = {http://www.enst.fr/~drossi/paper/rossi15conext.pdf}, author = {Cicalese, Danilo and Auge, Jordan and Joumblatt, Diana and Friedman, Tim ur and Rossi, Dario} } @article {D61, title = {Demonstration Plan}, number = {D6.1}, year = {2015}, month = {01/2015}, type = {Public Deliverable}, issn = {D6.1}, author = {Pedro Casas and Edion Tego and Francesco Matera and Maurizio Dusi and A. Bakay and Balazs Szabo and G. Rozsa and Stefano Traverso and Ilias Leontiadis and L. Baltrunas and Yan Grunenberger and Andrea Fregosi and A. Kahveci and Eike Kowallik and G. Mattellini and C. Meregalli and Stefano Raffaglio and M. Russo and Andrea Sannino and M. Scarpino} } @inproceedings {Cas2015b, title = {Exploring QoE in Cellular Networks: How Much Bandwidth do you Need for Popular Smartphone Apps?}, booktitle = {5th ACM SIGCOMM Workshop on All Things Cellular: Operations, Applications and Challenges}, year = {2015}, author = {Pedro Casas and Raimund Schatz and Florian Wamser and Michael Seufert and Ralf Irmer} } @inproceedings {DR:INFOCOM-15a, title = {A Fistful of Pings: Accurate and Lightweight Anycast Enumeration and Geolocation}, booktitle = {IEEE INFOCOM}, year = {2015}, month = {04/2015}, abstract = {

Use of IP-layer anycast has increased in the last few years: once relegated to DNS root and top-level domain servers, anycast is now commonly used to assist distribution of general purpose content by CDN providers. Yet, the measurement techniques for discovering anycast replicas have been designed around DNS, limiting their usefulness to this particular service. This raises the need for protocol agnostic methodologies, that should additionally be as lightweight as possible in order to scale up anycast service discovery. This is precisely the aim of this paper, which proposes a new method for exhaustive and accurate enumeration and city-level geolocation of anycast instances, requiring only a handful of latency measurements from a set of known vantage points. Our method exploits an iterative workflow that enumerates (an optimization problem) and geolocates (a classification problem) anycast replicas. We thoroughly validate our methodology on available ground truth (several DNS root servers), using multiple measurement infrastructures (PlanetLab, RIPE), obtaining extremely accurate results (even with simple algorithms, that we compare with the global optimum), that we make available to the scientific community. Compared to the state of the art work that appeared in INFOCOM 2013 and IMC 2013, our technique (i) is not bound to a specific protocol, (ii) requires 1000 times fewer vantage points, not only (iii) achieves over 50\% recall but also (iv) accurately identifies the city-level geolocation for over 78\% of the enumerated servers, with (v) a mean geolocation error of 361 km for all enumerated servers.

}, url = {http://www.enst.fr/~drossi/paper/rossi15infocom.pdf}, author = {Cicalese, Danilo and Joumblatt, Diana and Rossi, Dario and Buob, Marc-Olivier and Auge, Jordan and Friedman, Timur} } @article {2016, title = {HFSP: Bringing size-based scheduling to Hadoop}, journal = {IEEE Transaction on Cloud Computing}, year = {2015}, author = {Mario Pastorelli and Damiano Carra and Matteo Dell{\textquoteright}Amico and Pietro Michiardi} } @inproceedings {DR:INFOCOM-15b, title = {A Lightweight Anycast Enumeration and Geolocation}, booktitle = {IEEE INFOCOM, Demo Session}, year = {2015}, address = {Hong Kong, China}, abstract = {

Several Internet services such as CDNs, DNS name servers, and sinkholes use IP-layer anycast to reduce user response times and increase robustness with respect to network failures and denial of service attacks. However, current geolocation tools fail with anycast IP addresses. In our recent work [1], we remedy to this by developing an anycast detection, enumeration, and geolocation technique based on a set of delay measurements from a handful of geographically distributed vantage points. The technique (i) detects if an IP is anycast, (ii) enumerates replicas by finding the maximum set of non-overlapping disks (i.e., areas centered around vantage points), and (iii) geolocates the replicas by solving a classification problem and assigning the server location to the most likely city. We propose to demo this technique. In particular, we visually show how to detect an anycast IP, enumerate its replicas, and geolocate them on a map. The demo allows to browse previously geolocated services, as well as to explore new targets on demand.

}, url = {http://www.enst.fr/~drossi/paper/rossi15infocom-b.pdf}, author = {Cicalese, Danilo and Joumblatt, Diana and Rossi, Dario and Buob, Marc-Olivier and Auge, Jordan and Friedman, Timur} } @inproceedings {Seu2015a, title = {On the Monitoring of YouTube QoE in Cellular Networks from End-devices}, booktitle = {Seventh ACM S3 Workshop}, year = {2015}, author = {Michael Seufert and Florian Wamser and Pedro Casas and Ralf Irmer and Phuoc Tran-Gia and Raimund Schatz} } @inproceedings {bar2015m, title = {MTRAC - Discovering M2M Devices in Cellular Networks from Coarse-grained Measurements}, booktitle = {IEEE International Conference on Communications (ICC)}, year = {2015}, author = {Arian B{\"a}r and Philippe Svoboda and Pedro Casas} } @inproceedings {Pap2015, title = {Nonparametric Statistical Methods to Analyze the Internet Connectivity Reliability}, booktitle = {IEEE International Conference on Communications Quality and Reliability (CQR 2015)}, year = {2015}, author = {Dimitri Papadimitriou and Davide Careglio} } @inproceedings {7145326, title = {Online Social Networks anatomy: On the analysis of Facebook and WhatsApp in cellular networks}, booktitle = {IFIP Networking Conference (IFIP Networking), 2015}, year = {2015}, month = {May}, keywords = {Cellular Networks, Content Delivery Networks, Europe, Facebook, Internet, IP networks, Network Measurements, Online Social Networks, Organizations, Servers, WhatsApp}, doi = {10.1109/IFIPNetworking.2015.7145326}, author = {Fiadino, Pierdomenico and Casas, Pedro and Schiavone, Mirko and D{\textquoteright}Alconzo, Alessandro} } @inproceedings {Cas2015, title = {QoMOSN - On the Analysis of Traffic and Quality of Experience in Mobile Online Social Networks}, booktitle = {European Conference on Networks and Communications (EuCNC)}, year = {2015}, author = {Pedro Casas and Pierdomenico Fiadino and Mirko Schiavone} } @inproceedings {Fia2015a, title = {RCATool - A Framework for Detecting and Diagnosing Anomalies in Cellular Networks}, booktitle = {27th International Teletraffic Congress (ITC)}, year = {2015}, author = {Pierdomenico Fiadino and Alessandro D{\textquoteright}Alconzo and Mirko Schiavone and Pedro Casas} } @inproceedings {Cas2015d, title = {Taming QoE in Cellular Networks: from Subjective Lab Studies to Measurements in the Field}, booktitle = {IRTF \& ISOC Workshop on Research and Applications of Internet Measurements (RAIM)}, year = {2015}, month = {11/2015}, author = {P Casas and B Gardlo and M Seufert and F Wamser and R Schatz} } @inproceedings {Cas2015c, title = {Taming QoE in Cellular Networks: from Subjective Lab Studies to Measurements in the Field}, booktitle = {11th International Conference on Network and Service Management (CNSM)}, year = {2015}, month = {10/2015}, author = {P Casas and B Gardlo and M Seufert and F Wamser and R Schatz} } @inproceedings {Fia2015, title = {Towards Automatic Detection and Diagnosis of Internet Service Anomalies via DNS Traffic Analysis}, booktitle = {6th International Workshop on Traffic Analysis and Characterization (TRAC)}, year = {2015}, author = {Pierdomenico Fiadino and Alessandro D{\textquoteright}Alconzo and Mirko Schiavone and Pedro Casas} } @inproceedings {Was2015, title = {Towards DisNETPerf: a Distributed Internet Paths Performance Analyzer}, booktitle = {The 11th International Conference on emerging Networking EXperiments and Technologies - CoNEXT 2015}, year = {2015}, month = {11/2015}, abstract = {

For more than 25 years now, traceroute has demonstrated its supremacy for network-path measurement, becoming the most widely used Internet path diagnosis tool today. A major limitation of traceroute when the destination is not controllable by the user is its inability to measure reverse paths, i.e., the path from a destination back to the source. Proposed techniques to address this issue \ rely on IP address spoofing, which might lead to security concerns. In this paper we introduce and evaluate DisNETPerf, a new tool for locating probes that are the closest to a distant server. Those probes are then used to collect data from the server point-of-view to the service user for path performance monitoring and troubleshooting purposes. We propose two techniques for probe location, and demonstrate that the reverse path can be measured with very high accuracy in certain scenarios.

}, author = {Sarah Wassermann and Pedro Casas and Benoit Donnet} } @inproceedings {Wam2015a, title = {Understanding YouTube QoE in Cellular Networks with YoMoApp - a QoE Monitoring Tool for YouTube Mobile}, booktitle = {ACM MOBICOM}, year = {2015}, author = {Florian Wamser and Michael Seufert and Pedro Casas and Ralf Irmer and Phuoc Tran-Gia and Raimund Schatz} } @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 {Wam2015, title = {YoMoApp: a Tool for Analyzing QoE of YouTube HTTP Adaptive Streaming in Mobile Networks}, booktitle = {European Conference on Networks and Communications (EuCNC)}, year = {2015}, author = {Florian Wamser and Michael Seufert and Pedro Casas and Ralf Irmer and Phuoc Tran-Gia and Raimund Schatz} } @inproceedings {Seu2015, title = {YouTube QoE on Mobile Devices: Subjective Analysis of Classical vs. Adaptive Video Streaming}, booktitle = {6th International Workshop on Traffic Analysis and Characterization (TRAC)}, year = {2015}, author = {Michael Seufert and Florian Wamser and Pedro Casas and Ralf Irmer and Phuoc Tran-Gia and Raimund Schatz} } @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 = {

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.

}, 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 = {

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 {Cas2014e, title = {Characterizing Microsoft Lync Online in Mobile Networks: a Quality of Experience Perspective}, booktitle = {3rd IEEE International Conference on Cloud Networking}, year = {2014}, month = {10/2014}, publisher = {IEEE}, organization = {IEEE}, address = {Luxembourg, Luxembourg}, abstract = {

Cloud-based systems are gaining enormous popularity due to a number of promised benefits, including ease of deployment and administration, scalability and flexibility, and costs savings. However, as more personal and business applications migrate to the Cloud, the service quality becomes an important differentiator between providers. ISPs, Cloud providers and enterprises migrating their services to the Cloud must therefore understand the network requirements to ensure proper end-user Quality of Experience (QoE) in these services. This paper addresses the problem of QoE in Telepresence and Remote Collaboration (TRC) services provided by Microsoft Lync Online (MLO). MLO is a Cloud-based service providing online meeting capabilities including videoconferencing, audio calls, and desktop sharing, and has become the default system for TRC in enterprise scenarios. We present a complete study of the QoE undergone by 44 MLO users in controlled subjective lab tests. The study is performed on three different interactive scenarios running on top of the real MLO Cloud service, additionally shaping the Lync flows at the access network to influence the participants experience. The scenarios include audioconferencing, videoconferencing, and remote collaboration though desktop sharing. By passively monitoring the end-to-end QoS achieved by the Lync flows, and correlating it with the QoE feedbacks provided by the participants, this study permits to better understand the interplays between network performance and QoE in TRC Cloud services. In addition, we provide a network-level characterization of the traffic generated by MLO, as well as an overview on the infrastructure hosting MLO servers.

}, keywords = {Audioconferencing, Cloud QoE, Distributed Data Center, Microsoft Lync Online, MOS, Remote Desktop Sharing, Telepresence, Videoconferencing}, author = {Pedro Casas and Andreas Sackl and Sebastian Egger and Raimund Schatz} } @inproceedings {Fia, title = {Characterizing Web Services Provisioning via CDNs: The Case of Facebook}, booktitle = {5th International Workshop on TRaffic Analysis and Characterization}, year = {2014}, month = {08/2014}, publisher = {IEEE}, organization = {IEEE}, address = {Nicosia, Cyprus}, abstract = {

Today{\textquoteright}s Internet consists of massive scale web\ services and Content Delivery Networks (CDNs). This paper sheds\ light on the way major Internet-scale web services content is\ hosted and delivered. By analyzing a full month of HTTP traffic\ traces collected at the mobile network of a major European ISP,\ we characterize the paradigmatic case of Facebook, considering\ not only the traffic flows but also the main organizations and\ CDNs providing them. Our study serves the main purpose of\ better understanding how major web services are provisioned\ in today{\textquoteright}s Internet, paying special attention to the temporal\ dynamics of the service delivery and the interplays between the\ involved hosting organizations. To the best of our knowledge, this\ is the first paper providing such an analysis in mobile networks.

}, keywords = {Akamai, Content Delivery Networks, Facebook, HTTP Traffic, mobile networks}, author = {Pierdomenico Fiadino and Alessandro D{\textquoteright}Alconzo and Pedro Casas} } @article {D43, title = { Cross-check of Analysis Modules and Reasoner Interactions}, number = {D4.3}, year = {2014}, month = {10/2014}, type = {Deliverable}, keywords = {reasoner, WP4}, author = {Umberto Manferdini and Stefano Traverso and Marco Mellia and Edion Tego and Francesco Matera and Zied Ben Houidi and Marco Milanesio and Pietro Michiardi and Dario Rossi and D. Cicalese and D. Joumblatt and Jordan Aug{\'e} and Maurizio Dusi and Sofia Nikitaki and Mohamed Ahmed and Ilias Leontiadis and L. Baltrunas and M. Varvello and Pedro Casas and Alessandro D{\textquoteright}Alconzo and Benoit Donnet and W. Du and Guy Leduc and Y. Liao and Alessandro Capello and Fabrizio Invernizzi} } @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} } @article {D42, title = {Design of the Reasoner}, number = {D4.2}, year = {2014}, month = {06/2014}, type = {report}, keywords = {design, private deliverable, reasoner, WP4}, issn = {D4.2}, author = {Pedro Casas and Alessandro D{\textquoteright}Alconzo and Maurizio Dusi and Sofia Nikitaki and Mohamed Ahmed and Stefano Traverso and Marco Mellia and Daniele Apiletti and Luigi Grimaudo and Elena Baralis and Dario Rossi and D. Joumblatt and Alessandro Capello and M. D{\textquoteright}Ambrosio and Fabrizio Invernizzi and M. Ullio and Andrea Fregosi and Eike Kowallik and Stefano Raffaglio and Andrea Sannino and Marco Milanesio and Edion Tego and Francesco Matera and Tivadar Szemethy and Balazs Szabo and L. N{\'e}meth and Zied Ben Houidi and G. Dimopoulos and Ilias Leontiadis and Yan Grunenberger and L. Baltrunas and Michael Faath and Rolf Winter and Dimitri Papadimitriou} } @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 {Schiavone:2014:DDA:2680821.2680831, title = {Diagnosing Device-Specific Anomalies in Cellular Networks}, booktitle = {Proceedings of the 2014 CoNEXT on Student Workshop}, year = {2014}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, keywords = {anomaly diagnosis, cellular networks., entropy-based analysis}, isbn = {978-1-4503-3282-8}, doi = {10.1145/2680821.2680831}, url = {http://doi.acm.org/10.1145/2680821.2680831}, author = {Schiavone, Mirko and Romirer-Maierhofer, Peter and Fiadino, Pierdomenico and Casas, Pedro} } @inproceedings {Cas2014a, title = {Distributed active measurement of Internet queuing delays}, booktitle = {Passive and Active Measurement (PAM), Extended Abstract}, year = {2014}, month = {March}, address = {Los Angeles, USA}, abstract = {

Despite growing link capacities, over-dimensioned buffers are still causing, in the Internet of the second decade of the third millenium, hosts to suffer from severe queuing delays (or bufferbloat). While maximum bufferbloat possibly exceeds few seconds, it is far less clear how often this maximum is hit in practice. This paper reports on our ongoing work to build a spatial and temporal map of Internet bufferbloat, describing a system based on distributed agents running on PlanetLab that aims at providing a quantitative answer to the above question.

}, url = {http://www.enst.fr/~drossi/paper/rossi14pam-b.pdf}, author = {P. Casoria and D Rossi and Jordan Aug{\'e} and Marc-Oliver Buob and T. Friedman and A. Pescape} } @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} } @article {Tra, title = {mPlane: an Intelligent Measurement Plane for the Internet}, journal = {IEEE Communications Magazine, Special Issue on Monitoring and Troubleshooting Multi-domain Networks using Measurement Federations}, volume = {42}, year = {2014}, month = {05/2014}, author = {Brian Trammell and Pedro Casas and Dario Rossi and Arian B{\"a}r and Zied Ben-Houidi and Ilias Leontiadis and Tivadar Szemethy and Marco Mellia} } @article {rfc7119, title = {Operation of the IP Flow Information Export (IPFIX) Protocol on IPFIX Mediators (RFC 7119)}, year = {2014}, month = {02/2014}, abstract = {
This document specifies the operation of the IP Flow Information Export (IPFIX) protocol specific to IPFIX Mediators, including Template and Observation Point management, timing considerations, and other Mediator-specific concerns.
}, url = {http://www.ietf.org/rfc/rfc7119.txt}, author = {B. Claise and A. Kobayashi and B. Trammell} } @inproceedings {Cit2014, title = {On the Quality of BGP Route Collectors for iBGP Policy Inference}, booktitle = {IFIP Networking}, year = {2014}, month = {June 2014}, abstract = {

A significant portion of what is known about Internet routing stems out from\ public BGP datasets. For this reason, numerous research efforts were devoted to\ (i) assessing the (in)completeness of the datasets, (ii) identifying biases\ in the dataset, and (iii) augmenting data quality by optimally placing new\ collectors. However, those studies focused on techniques to extract information\ about the AS-level Internet topology.

In this paper, we show that considering different metrics influences the\ conclusions about biases and collector placement. Namely, we compare AS-level\ topology discovery with \iac inference. We find that the same datasets exhibit\ significantly diverse biases for these two metrics. For example, the sensitivity\ to the number and position of collectors is noticeably different. Moreover, for\ both metrics, the marginal utility of adding a new collector is strongly\ localized with respect to the proximity of the collector. Our results suggest\ that the {\textquoteleft}{\textquoteleft}optimal{\textquoteright}{\textquoteright} position for new collectors can only be defined with\ respect to a specific metric, hence posing a fundamental trade-off for\ maximizing the utility of extensions to the BGP data collection infrastructure.

}, keywords = {bias, iBGP policies, measurement, network topology}, author = {Luca Cittadini and Stefano Vissichio and Benoit Donnet} } @inproceedings {EURECOM+4268, title = {Revisiting size-based scheduling with estimated job sizes}, booktitle = {{MASCOTS} 2014, {IEEE} 22nd {I}nternational {S}ymposium on {M}odeling analysis and simulation of computer and telecommunication systems, {S}eptember 9-11, 2014, {P}aris, {F}rance / {A}lso published on {A}r{X}iv}, year = {2014}, month = {03}, address = {{P}aris, {FRANCE}}, url = {http://www.eurecom.fr/publication/4268}, author = {Dell{\textquoteright}Amico, Matteo and Carra, Damiano and Pastorelli, Mario and Michiardi, Pietro} } @inproceedings {Col2014, title = {Scalable accurate consolidation of passively measured statistical data}, booktitle = {Passive and Active Measurement (PAM), Extended Abstract}, year = {2014}, month = {March}, address = {Los Angeles, USA}, abstract = {

Passive probes continuously collect a significant amount of traffic vol- ume, and autonomously generate statistics on a large number of metrics. A common statistical output of passive probe is represented by 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. Taking an experimental approach, we study both cases assessing the impact of different consolidation strategies, obtaining general design and tuning guidelines.

}, url = {http://perso.telecom-paristech.fr/~drossi/paper/rossi14pam-a.pdf}, author = {S. Colabrese and D Rossi and Marco Mellia} } @inproceedings {Cas2014, title = {Understanding HTTP Traffic and CDN Behavior from the Eyes of a Mobile ISP}, booktitle = {Passive and Active Measurements Conference (PAM)}, year = {2014}, author = {Pedro Casas and Pierdomenico Fiadino and Arian B{\"a}r} } @inproceedings {Fiaa, title = {Vivisecting WhatsApp through Large-Scale Measurements in Mobile Networks}, booktitle = {SIGCOMM 2014}, year = {2014}, month = {08/2014}, publisher = {ACM}, organization = {ACM}, address = {Chicago, USA}, abstract = {

WhatsApp, the new giant in instant multimedia messaging in mobile networks is rapidly increasing its popularity, taking over the traditional SMS/MMS messaging. In this paper we present the first large-scale characterization of WhatsApp, useful among others to ISPs willing to understand the impacts of this and similar applications on their networks. Through the combined analysis of passive measurements at the core of a national mobile network, worldwide geo-distributed active measurements, and traffic analysis at end devices, we show that: (i) the WhatsApp hosting architecture is highly centralized and exclusively located in the US; (ii) video sharing covers almost 40\% of the total WhatsApp traffic volume; (iii) flow characteristics depend on the OS of the end device; (iv) despite the big latencies to US servers, download throughputs are as high as 1.5 Mbps; (v) users react immediately and negatively to service outages through social networks feedbacks.

}, keywords = {Large-Scale Measurements, mobile networks, WhatsApp}, author = {Pierdomenico Fiadino and Mirko Schiavone and Pedro Casas} } @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 {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} } @inproceedings {Cas2014c, title = {YouTube in the Move: Understanding the Performance of YouTube in Cellular Networks (BEST PAPER AWARD RUNNER UP)}, booktitle = {Wireless Days 2014}, year = {2014}, month = {11/2014}, publisher = {IEEE}, organization = {IEEE}, address = {Rio de Janeiro, Brazil}, abstract = {

YouTube is the most popular and volume-dominant service in today{\textquoteright}s Internet, and is changing the way ISPs manage their networks. Understanding the performance of YouTube traffic is paramount for ISPs, specially for mobile operators, who must handle the huge surge of traffic with the constraints and challenges of cellular networks. In this paper we present an empirical analysis of the performance of YouTube flows accessed through a national-wide cellular network, considering download throughput as well as end-user Quality of Experience (QoE) metrics. The analysis considers the characteristics and impacts of the Content Delivery Network hosting YouTube, and compares its behavior with other popular HTTP video streaming services accessed through cellular networks. The QoE analysis is performed through end-user device measurements, which directly reflect the experience of the end-users. Our study additionally shows the potentiality of monitoring YouTube performance in cellular networks directly from the smart-phones of the users, bypassing the traffic visibility loss at the core of the network introduced by traffic encryption (e.g., HTTPS).

}, keywords = {Cellular Networks, Content Delivery Networks, End-device Measurements, QoE, Traffic Measurements, YouTube}, author = {Pedro Casas and Pierdomenico Fiadino and Andreas Sackl and Alessandro D{\textquoteright}Alconzo} } @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 {Fia2013, title = {HTTPTag: A Flexible On-line HTTP Classification System for Operational 3G Networks}, booktitle = {INFOCOM{\textquoteright}2013 Demo/Poster Session (INFOCOM{\textquoteright}2013 - Demo/Poster Session)}, year = {2013}, address = {Turin, Italy}, abstract = {

The popularity of web-based services and applications like YouTube and Facebook has taken HTTP back to the pole position on end-user traffic consumption. We present HTTPTag, a flexible on-line HTTP classification system based on pattern matching and tagging. HTTPTag recognizes on the fly and tracks the evolution of more than 280 applications running on top of HTTP in an operational 3G network, representing more than 70\% of the total HTTP traffic volume consumed by its customers. HTTPTag improves the network traffic visibility of an operator, performing tasks such as top-services ranking, long-term monitoring of applications popularity, and trend analysis among others.

}, keywords = {3G Networks, HTTP, Pattern Matching, Traffic Classification}, author = {Pierdomenico Fiadino and A B{\"a}r and P. Casas} } @inproceedings {Cas2013, title = {IP Mining: Extracting Knowledge from the Dynamics of the Internet Addressing Space (BEST PAPER AWARD)}, booktitle = {25th International Teletraffic Congress, ITC 25}, year = {2013}, abstract = {

Going back to the Internet of one decade ago, HTTP-based content and web services were provided by centralized or barely distributed servers. Single hosts providing exclusive services at fixed IP addresses was the standard approach. Current situation has drastically changed, and the mapping of IPs to different content and services is nowadays extremely dynamic. The adoption of large CDNs by major Internet players, the extended usage of transparent content caching, the explosion of Cloud-based services, and the decoupling between content providers and the hosting infrastructure have created a difficult to manage Internet landscape. Understanding such a complex scenario is paramount for network operators, both to control the traffic on their networks and to improve the quality experienced by their customers, specially when something goes wrong. Using a full week of HTTP traffic traces collected at the mobile broadband network of a major European ISP, this paper studies the associations between web services, the hosting organizations-ASes, and the content servers{\textquoteright} IPs. By mining correlations among these, we extract useful insights about the dynamics of the IP addressing space used by the top web services, and the way content providers and hosting organizations deliver their services to the mobile endusers. The extracted knowledge is applied on two specific use-cases, the former on hosting and service delivery characterization, the latter on automatic IP-based HTTP services classification.

}, keywords = {Content Delivery Networks, HTTP Traffic, IP Addressing Space, Traffic Classification and Analysis}, doi = {10.1109/ITC.2013.6662933}, author = {Pedro Casas and Pierdomenico Fiadino and Arian B{\"a}r} } @inbook {Pap2013b, title = {Measurement-based Experimental Research Methodology}, booktitle = {Measurement Methodology and Tools}, series = {Lecture Notes in Computer Science Series}, volume = {7586}, year = {2013}, pages = {pp 5-22}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, issn = {978-3-642-41295-0}, doi = {10.1007/978-3-642-41296-7_2}, author = {D. Papadimitriou and L. F{\`a}brega and P. Vil{\`a} and D. Careglio and P. Demeester} } @inproceedings {Cas2013c, title = {Mini-IPC: A Minimalist Approach for HTTP Traffic Classification using IP Addresses}, booktitle = {4th International Workshop on Traffic Analysis and Classification, TRAC 2013}, year = {2013}, abstract = {

The popularity of web-based services and multimedia applications like YouTube, Google Web Search, Facebook, and a bewildering range of Internet applications has taken HTTP back to the pole position on end-user traffic consumption. Today{\textquoteright}s Internet users exchange most of their content via HTTP. In this paper we address the problem of on-line HTTP traffic classification from network measurements. Building on the results provided by HTTPTag, a flexible system for on-line HTTP classification, we present and explore Mini-IPC. Mini-IPC is a minimalist approach for classifying HTTP flows using only the IP addresses of the servers hosting the corresponding content. Using one full week of HTTP traffic traces collected at the mobile broadband network of a major European ISP, we investigate to which extent the most popular HTTP-based services are hosted by well-defined sets of IP addresses, and evaluate the performance of Mini-IPC to classify these services using IPs only.

}, keywords = {CDNs, HTTP Traffic, IP Addressing Space, Mobile Networks{\textquoteright} Traffic, Traffic Classification and Analysis}, doi = {10.1109/IWCMC.2013.6583537}, author = {P. Casas and Pierdomenico Fiadino} } @inproceedings {Chi2013, title = {Passive bufferbloat measurement exploiting transport layer information}, booktitle = {IEEE GLOBECOM}, year = {2013}, month = {12/2013}, url = {http://www.enst.fr/~drossi/paper/rossi13globecom.pdf}, author = {C. Chirichella and D Rossi and C. Testa and T. Friedman and A. Pescape} } @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} } @inproceedings {Aru2013, title = {Quality-of-Experience driven Acceleration of Thin Client Connections}, booktitle = {IEEE International Symposium on Network Computing and Applications}, year = {2013}, month = {08/2013}, edition = {12}, doi = {10.1109/NCA.2013.26}, author = {Mayutan Arumaithurai and Jan Seedorf and Maurizio Dusi and Edo Monticelli and Renato Lo Cigno} } @inproceedings {Chi2013c, title = {Remotely Gauging Upstream Bufferbloat Delays}, booktitle = {Passive and Active Measurement (PAM)}, year = {2013}, abstract = { {\textquoteleft}{\textquoteleft}Bufferbloat{\textquoteright}{\textquoteright} is the growth in buffer size that has led Internet delays to occasionally exceed the light propagation delay from the Earth to the Moon. Manufacturers have built in large buffers to prevent losses on Wi-Fi, cable and ADSL links. But the combination of some links{\textquoteright} limited bandwidth with TCP{\textquoteright}s tendency to saturate that bandwidth results in excessive queuing delays. In response, new congestion control protocols such as BitTorrent{\textquoteright}s uTP/LEDBAT aim at explicitly limiting the delay that they add over the bottleneck link. This work proposes and validate a methodology to monitor the upstream queuing delay experienced by remote hosts, both those using LEDBAT, through LEDBAT{\textquoteright}s native one-way delay measurements, and those using TCP (via the Time-stamp Option). }, doi = {10.1007/978-3-642-36516-4_25}, url = {http://www.enst.fr/~drossi/paper/rossi13pam.pdf}, author = {C. Chirichella and D Rossi and C. Testa and T. Friedman and A. Pescape} } @inproceedings {Val2013, title = {RILAnalyzer: a Comprehensive 3G Monitor on Your Phone}, booktitle = {Internet Measurement Conference (IMC)}, year = {2013}, doi = {10.1145/2504730.2504764}, author = {Narseo Vallina-Rodriguez and A. Aucinas and M. Almeida and Yan Grunenberger and Konstantina Papagiannaki and Jon Crowcroft} } @inproceedings {Api2013, title = {SEARUM: a cloud-based SErvice for Association RUle Mining}, booktitle = {The 11th IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA-13)}, year = {2013}, abstract = {

Large volumes of data are being produced by various modern applications at an ever increasing rate. These applications range from wireless sensors networks to social networks. The automatic analysis of such huge data volume is a challenging task since a large amount of interesting knowledge can be extracted. Association rule mining is an exploratory data analysis method able to discover interesting and hidden correlations among data. Since this data mining process is characterized by computationally intensive tasks, efficient distributed approaches are needed to increase its scalability. This paper proposes a novel cloud-based service, named SEARUM, to efficiently mine association rules on a distributed computing model. SEARUM consists of a series of distributed MapReduce jobs run in the cloud. Each job performs a different step in the association rule mining process. As a case study, the proposed approach has been applied to the network data scenario. The experimental validation, performed on two real network datasets, shows the effectiveness and the efficiency of\ SEARUM in mining association rules on a distributed computing model.

}, keywords = {association rule mining, cloud-based service, distributed computing model, network data analysis}, doi = {10.1109/TrustCom.2013.153}, author = {Daniele Apiletti and Elena Baralis and Tania Cerquitelli and Silvia Chiusano and Luigi Grimaudo} } @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 {Chi2013a, title = {To the Moon and back: are Internet bufferbloat delays really that large}, booktitle = {IEEE INFOCOM Workshop on Traffic Monitoring and Analysis (TMA{\textquoteright}13)}, year = {2013}, doi = {10.1109/INFCOMW.2013.6562886}, url = {http://www.enst.fr/~drossi/paper/rossi13tma-a.pdf}, author = {C. Chirichella and D Rossi} } @article {Tra2013a, title = {Use Case Elaboration and Requirements Specification}, number = {D1.1}, year = {2013}, month = {01/2013}, institution = {mPlane Consortium}, type = {Public Deliverable}, address = {Torino}, abstract = {

The document defines the requirements for the mPlane architecture on the background of a set of scenarios explored by the consortium, a survey of existing comparable measurement systems and platforms and applicable standards therefore, and a set of architectural first principles drawn from the description of work and the consortium{\textquoteright}s experience.\ As mPlane is intended to be a fully flexible measurement platform, freely integrating existing probes and repositories with ones to be developed in the project, this document is primarily concerned with the definition of interfaces among mPlane components. While it does enumerate capabilities to be provided by these components, these are primarily intended to ensure the platform has the flexibility required to meet all the scenarios envisioned; the enumerations of measurements, metrics, data types, and other component capabilities are therefore not to be construed to limit the scope of work on components within the project to just those scenarios treated in this document; nor do the scenarios enumerated here define the capabilities to be demonstrated in the project{\textquoteright}s integrated trial.\ 

}, keywords = {architecture, measurement, platform, scenario, use case}, issn = {D1.1}, author = {Brian Trammell and Stephan Neuhaus and Francesco Matera and Ernst Biersack and Antonio Barbuzzi and Saverio Niccolini and Mohamed Ahmed and Maurizio Dusi and Tivadar Szemethy and Balazs Szabo and P. Casas and A B{\"a}r and Konstantina Papagiannaki and Yan Grunenberger and Ilias Leontiadis and Rolf Winter and Zied Ben-Houidi and Giovanna Carofiglio and Samir Ghamri-Doudane and Diego Perino and D Rossi} } @inproceedings {Cas2013a, title = {YOUQMON: A System for On-line Monitoring of YouTube QoE in Operational 3G Networks}, booktitle = {31st IFIP Performance}, year = {2013}, abstract = {

YouTube is changing the way operators manage network performance monitoring. In this paper we introduce YOUQMON, a novel on-line monitoring system for assessing the Quality of Experience (QoE) undergone by HSPA/3G customers watching YouTube videos, using network-layer measurements only. YOUQMON combines passive traffic analysis techniques to detect stalling events in YouTube video streams, with a QoE model to map stallings into a Mean Opinion Score reflecting the end-user experience. We evaluate the stalling detection performance of YOUQMON with hundreds of YouTube video streams, and present results showing the feasibility of performing real-time YouTube QoE monitoring in an operational mobile broadband network.

}, keywords = {3G Networks, MOS, QoE Monitoring, Stallings, YouTube}, doi = {10.1145/2518025.2518033}, author = {P. Casas and M. Seufert and R. Schatz} } @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} }