@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 {TrKu15, title = {Enabling Internet-Wide Deployment of Explicit Congestion Notification}, booktitle = {Proceedings of the 2015 Passive and Active Measurement Conference}, year = {2015}, month = {03/2015}, address = {New York}, abstract = {

\ac{ECN} is an TCP/IP extension to signal network congestion without packet loss, which has barely seen deployment though it was standardized and implemented more than a decade ago. On-going activities in research and standardization aim to make the usage of \ac{ECN} more beneficial. This measurement study provides an update on deployment status and newly assesses the marginal risk of enabling \ac{ECN} negotiation by default on client end-systems. Additionally, we dig deeper into causes of connectivity and negotiation issues linked to \ac{ECN}. We find that about five websites per thousand suffer additional connection setup latency when fallback per RFC 3168 is correctly implemented; we provide a patch for Linux to properly perform this fallback. Moreover, we detect and explore a number of cases in which \ac{ECN} brokenness is clearly path-dependent, i.e. on middleboxes beyond the access or content provider network. Further analysis of these cases can guide their elimination, further reducing the risk of enabling \ac{ECN} by default.

}, author = {Brian Trammell and Mirja K{\"u}hlewind and Damiano Boppart and Iain Learmonth and Gorry Fairhurst and Richard Scheffenegger} } @inproceedings {TrKuPAM15, title = {Enabling Internet-Wide Deployment of Explicit Congestion Notification}, booktitle = {Proceedings of the 2015 Passive and Active Measurement Conference}, year = {2015}, month = {Mar}, address = {New York}, url = {http://ecn.ethz.ch/ecn-pam15.pdf}, author = {Brian Trammell and Mirja K{\"u}hlewind and Damiano Boppart and Iain Learmonth and Gorry Fairhurst and Richard Scheffenegger} } @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 {Dim2015, title = {Identifying the Root Cause of Video Streaming Issues on Mobile Devices}, booktitle = {CoNext}, year = {2015}, author = {G. Dimopoulos and I. Leontiadis and P. Barlet-Ros and K. Papagiannaki and P. Steenkiste} } @inproceedings {DR:TRAC-15, title = {Impact of Carrier-Grade NAT on Web Browsing}, booktitle = { 6th International Workshop on TRaffic Analysis and Characterization (TRAC) - The paper won the BEST PAPER award}, year = {2015}, month = {08/2015}, publisher = {IEEE}, organization = {IEEE}, address = {Dobrovnik, Croatia}, abstract = {

Public IPv4 addresses are a scarce resource. While IPv6 adoption is lagging, Network Address Translation (NAT) technologies have been deployed over the last years to alleviate IPv4 exiguity and their high rental cost. In particular, Carrier- Grade NAT (CGN) is a well known solution to mask a whole ISP network behind a limited amount of public IP addresses, significantly reducing expenses. Despite its economical benefits, CGN can introduce connectiv- ity issues which have sprouted a considerable effort in research, development and standardization. However, to the best of our knowledge, little effort has been dedicated to investigate the impact that CGN deployment may have on users{\textquoteright} traffic. This paper fills the gap. We leverage passive measurements from an ISP network deploying CGN and, by means of the Jensen- Shannon divergence, we contrast several performance metrics considering customers being offered public or private addresses. In particular, we gauge the impact of CGN presence on users{\textquoteright} web browsing experience. Our results testify that CGN is a mature and stable technology as, if properly deployed, it does not harm users{\textquoteright} web browsing experience. Indeed, while our analysis lets emerge expected stochastic differences of certain indexes (e.g., the difference in the path hop count), the measurements related to the quality of users{\textquoteright} browsing are otherwise unperturbed. Interestingly, we also observe that CGN protects customers from unsolicited, often malicious, traffic.

}, url = {http://www.enst.fr/~drossi/paper/rossi15trac.pdf}, author = {Bocchi, Enrico and Safari, Ali and Traverso, Stefano and Finamore, Alessandro and Di Gennaro, Valeria and Mellia, Marco and Munafo, Maurizio and Rossi, Dario} } @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 {Nay2015, title = {multi-context TLS (mcTLS): Enabling Secure In-Network Functionality in TLS}, booktitle = {2015 ACM SIGCOMM Conference (SIGCOMM {\textquoteright}15)}, year = {2015}, publisher = {ACM}, organization = {ACM}, address = {London}, abstract = {

Transport Layer Security (TLS), is the de facto protocol supporting secure HTTP (HTTPS), and is being discussed as the default transport protocol for HTTP2.0. It has seen wide adoption and is currently carrying a significant fraction of the overall HTTP traffic (Facebook, Google and Twitter use it by default). However, TLS makes the fundamental assumption that all functionality resides solely at the endpoints, and is thus unable to utilize the many in-network services that optimize network resource usage, improve user experience, and protect clients and servers from security threats. Re-introducing such in-network functionality into secure TLS sessions today is done through hacks, in many cases weakening overall security.

In this paper we introduce multi-context TLS (mcTLS) which enhances TLS by allowing middleboxes to be fully supported participants in TLS sessions. mcTLS breaks the "all-or-nothing" security model by allowing endpoints and content providers to explicitly introduce middleboxes in secure end-to-end sessions, while deciding whether they should have read or write access, and to which specific parts of the content. mcTLS enables transparency and control for both clients and servers.

We evaluate a prototype mcTLS implementation in both controlled and "live" experiments, showing that the benefits offered have minimal overhead.More importantly, we show that mcTLS can be incrementally deployed and requires small changes to clients, servers, and middleboxes, for a large number of use cases.

}, author = {David Naylor and Kyle Schomp and Matteo Varvello and Ilias Leontiadis and Jeremy Blackburn and Diego Lopez and Konstantina Papagiannaki and Pablo Rodriguez and Peter Steenkiste} } @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 {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} } @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 {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 {Boc2014, title = {Cloud Storage Service Benchmarking: Methodologies and Experimentations}, booktitle = {3rd IEEE International Conference on Cloud Networking (IEEE CloudNet 2014)}, year = {2014}, month = {10/2014}, publisher = {IEEE}, organization = {IEEE}, address = {Luxembourg}, abstract = {

Data storage is one of today{\textquoteright}s fundamental services with companies, universities and research centers having the need of storing large amounts of data every day. Cloud storage services are emerging as strong alternative to local storage, allowing customers to save costs of buying and maintaining expensive hardware. Several solutions are available on the market, the most famous being Amazon S3. However it is rather difficult to access information about each service architecture, performance, and pricing. To shed light on storage services from the customer perspective, we propose a benchmarking methodology, apply it to four popular offers (Amazon S3, Amazon Glacier, Windows Azure Blob and Rackspace Cloud Files), and compare their performance. Each service is analysed as a black box and benchmarked through crafted workloads. We take the perspective of a customer located in Europe, looking for possible service providers and the optimal data center where to deploy its applications. At last, we complement the analysis by comparing the actual and forecast costs faced when using each service.

According to collected results, all services show eventual weaknesses related to some workload, with no all-round eligible winner, e.g., some offers providing excellent or poor performance when exchanging large or small files. For all services, it is of paramount importance to accurately select the data center to where deploy the applications, with throughput that varies by factors from 2x to 10x. The methodology (and tools implementing it) here presented is instrumental for potential customers to identify the most suitable offer for their needs.\ 

}, keywords = {Amazon S3, Benchmarking, Cloud storage, Performance measurement, Web services, Windows Azure}, author = {Enrico Bocchi and Marco Mellia and Sofiane Sarni} } @inproceedings {Nay2014, title = {The Cost of the {\textquotedblleft}S{\textquotedblright} in HTTPS}, booktitle = {ACM Conference on emerging Networking EXperiments and Technologies (CoNEXT)}, year = {2014}, month = {12/2014}, author = {David Naylor and Alessandro Finamore and Ilias Leontiadis and Yan Grunenberger and Marco Mellia and Kostantina Papagiannaki and Peter Steenkiste} } @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 {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 {Leo2014, title = {From Cells to Streets: Estimating Mobile Paths with Cellular-Side Data}, booktitle = {CoNEXT}, year = {2014}, publisher = {ACM}, organization = {ACM}, address = {Sydney, Australia}, author = {Ilias Leontiadis and Antonio Lima and Haewoon Kwak and Rade Stanojevic and David Wetherall and Konstantina Papagiannaki} } @inproceedings {Ben2014, title = {Gold mining in a River of Internet Content Traffic}, booktitle = {6th International Workshop on Traffic Monitoring and Analysis, TMA}, year = {2014}, month = {04/2014}, publisher = {Springer}, organization = {Springer}, address = {London}, abstract = {With the advent of Over-The-Top content providers (OTTs), Internet Service Providers (ISPs) saw their portfolio of services shrink to the low margin role of data transporters. In order to counter this effect, some ISPs started to follow big OTTs like Facebook and Google in trying to turn their data into a valuable asset. In this paper, we explore the questions of what meaningful information can be extracted from network data, and what interesting insights it can provide. To this end, we tackle the first challenge of detecting {\textquotedblleft}user-URLs{\textquotedblright}, i.e., those links that were clicked by users as opposed to those objects automatically downloaded by browsers and applications. We devise algorithms to pinpoint such URLs, and validate them on manually collected ground truth traces. We then apply them on a three-day long traffic trace spanning more than 19,000 residential users that generated around 190 million HTTP transactions. We find that only 1.6\% of these observed URLs were actually clicked by users. As a first application for our methods, we answer the question of which platforms participate most in promoting the Internet content. Surprisingly, we find that, despite its notoriety, only 11\% of the user URL visits are coming from Google Search. }, keywords = {Content mining, HTTP Traffic, URL extraction}, author = {Zied Ben-Houidi and Giuseppe Scavo and Samir Ghamri-Doudane and Alessandro Finamore and Stefano Traverso and Marco Mellia} } @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 {Dus2014, title = {Reactive Logic in Software-Defined Networking: Measuring Flow-Table Requirements}, booktitle = {5th International Workshop on TRaffic Analysis and Characterization (TRAC)}, year = {2014}, month = {2014}, address = {Nicosia, Cyprus}, author = {Maurizio Dusi and Roberto Bifulco and Francesco Gringoli and Fabian Schneider} } @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 {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} } @inproceedings {Dra2013, title = {Benchmarking Personal Cloud Storage}, booktitle = {Internet Measurement Conference - IMC}, year = {2013}, month = {10/2013}, publisher = {ACM}, organization = {ACM}, address = {Barcelona (ES)}, abstract = {

Personal cloud storage services are data-intensive applica- tions already producing a significant share of Internet traffic. Several solutions offered by different companies attract more and more people. However, little is known about each service capabilities, architecture and {\textendash} most of all {\textendash} performance implications of design choices. This paper presents a methodology to study cloud storage services. We apply our methodology to compare 5 popular offers, revealing different system architectures and capabilities. The implications on performance of different designs are assessed executing a series of benchmarks. Our results show no clear winner, with all services suffering from some limitations or having potential for improvement. In some scenarios, the upload of the same file set can take seven times more, wasting twice as much capacity. Our methodology and results are useful thus as both benchmark and guideline for system design.

}, keywords = {Active Measurements, Personal Cloud Storage}, doi = {10.1145/2504730.2504762}, url = {http://www.simpleweb.org/wiki/Cloud_benchmarks}, author = {Idilio Drago and Enrico Bocchi and Marco Mellia and Herman Slatman and Aiko Pras} } @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} } @article {Tra2013d, title = {mPlane Architecture Specification}, number = {D1.3}, year = {2013}, month = {11/2013}, institution = {mPlane Consortium}, type = {Public Deliverable}, address = {Torino}, keywords = {architecture, measurement, platform, scenario, use case}, author = {Brian Trammell and Marco Mellia and Alessandro Finamore and Stefano Traverso and Tivadar Szemethy and Balazs Szabo and D Rossi and Benoit Donnet and Fabrizio Invernizzi and Dimitri Papadimitriou} } @article {Dus2013, title = {Plans for Using and Disseminating mPlane Knowledge}, number = {D7.2}, year = {2013}, month = {11/2013}, institution = {mPlane Consortium}, type = {Public Deliverable}, address = {Torino}, keywords = {dissemination, exploitation, open-source software, publications, standardization}, issn = {D7.2}, author = {Maurizio Dusi and Saverio Niccolini and Antonio Barbuzzi and M. Milanesio and Alessandro Finamore and Brian Trammell and Edion Tego and Francesco Matera and Tivadar Szemethy and Rolf Winter and Zied Ben-Houidi and Andrea Fregosi and Benoit Donnet and Fabrizio Invernizzi and Dimitri Papadimitriou and Ilias Leontiadis and Yan Grunenberger and P. Casas} } @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} } @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} } @article {Dav2013, title = {Stream-monitoring with blockmon: convergence of network measurements and data analytics platforms}, journal = {SIGCOMM Comput. Commun. Rev.}, volume = {43}, year = {2013}, pages = {29{\textendash}36}, keywords = {data analysis, distributed computing, performance analysis}, issn = {0146-4833}, doi = {10.1145/2479957.2479962}, url = {http://doi.acm.org/10.1145/2479957.2479962}, author = {Simoncelli, Davide and Maurizio Dusi and Francesco Gringoli and Saverio Niccolini} } @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} } @inproceedings {Tra2012, title = {On Flow Concurrency in the Internet and its Implications for Capacity Sharing}, booktitle = {Proceedings of the Second ACM CoNext Capacity Sharing Workshop (CSWS)}, year = {2012}, month = {12/2012}, address = {Nice, France}, doi = {10.1145/2413219.2413225}, author = {Brian Trammell and Dominik Schatzmann} } @inproceedings {Dra2012, title = {Inside Dropbox: Understanding Personal Cloud Storage Services}, booktitle = {Internet Measurement Conference - IMC}, year = {2012}, month = {11/2012}, publisher = {ACM}, organization = {ACM}, address = {Boston, MA}, abstract = {

Personal cloud storage services are gaining popularity. With a rush of providers to enter the market and an increasing of- fer of cheap storage space, it is to be expected that cloud storage will soon generate a high amount of Internet traffic. Very little is known about the architecture and the perfor- mance of such systems, and the workload they have to face. This understanding is essential for designing efficient cloud storage systems and predicting their impact on the network.

This paper presents a characterization of Dropbox, the leading solution in personal cloud storage in our datasets. By means of passive measurements, we analyze data from four vantage points in Europe, collected during 42 consecu- tive days. Our contributions are threefold: Firstly, we are the first to study Dropbox, which we show to be the most widely-used cloud storage system, already accounting for a volume equivalent to around one third of the YouTube traffic at campus networks on some days. Secondly, we character- ize the workload users in different environments generate to the system, highlighting how this reflects on network traf- fic. Lastly, our results show possible performance bottle- necks caused by both the current system architecture and the storage protocol. This is exacerbated for users connected far from storage data-centers.

All measurements used in our analyses are publicly avail- able in anonymized form at the SimpleWeb trace repository: http://traces.simpleweb.org/dropbox/

}, keywords = {Dropbox, passive measurement}, issn = {978-1-4503-1705-4}, doi = {10.1145/2398776.2398827}, url = {http://dl.acm.org/citation.cfm?id=2398776.2398827\&coll=DL\&dl=GUIDE\&CFID=225051145\&CFTOKEN=42401286}, author = {Idilio Drago and Marco Mellia and Maurizio M Munafo{\textquoteright} and Anna Sperotto and Ramin Sadre and Aiko Pras} } @inproceedings {Rio2012, title = {Wire-speed statistical classification of network traffic on commodity hardware}, booktitle = {ACM Internet Measurement Conference (IMC)}, year = {2012}, month = {11/2012}, abstract = {

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

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