@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 {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} } @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 {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 {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 {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 {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} } @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 {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} } @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} } @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} } @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} } @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} } @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 {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} }