<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pedro Casas</style></author><author><style face="normal" font="default" size="100%">Pierdomenico Fiadino</style></author><author><style face="normal" font="default" size="100%">Sarah Wassermann</style></author><author><style face="normal" font="default" size="100%">Stefano Traverso</style></author><author><style face="normal" font="default" size="100%">Alessandro D'Alconzo</style></author><author><style face="normal" font="default" size="100%">Edion Tego</style></author><author><style face="normal" font="default" size="100%">Francesco Matera</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Unveiling Network and Service Performance Degradation in the Wild with mPlane</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Communications Magazine - Network Testing Series</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Enrico Bocchi</style></author><author><style face="normal" font="default" size="100%">Idilio Drago</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Personal Cloud Storage Benchmarks and Comparison</style></title><secondary-title><style face="normal" font="default" size="100%">Cloud Computing, IEEE Transactions on</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Benchmark testing</style></keyword><keyword><style  face="normal" font="default" size="100%">Cloud computing</style></keyword><keyword><style  face="normal" font="default" size="100%">Cloud storage</style></keyword><keyword><style  face="normal" font="default" size="100%">Computers</style></keyword><keyword><style  face="normal" font="default" size="100%">Google</style></keyword><keyword><style  face="normal" font="default" size="100%">Measurements</style></keyword><keyword><style  face="normal" font="default" size="100%">Performance</style></keyword><keyword><style  face="normal" font="default" size="100%">Servers</style></keyword><keyword><style  face="normal" font="default" size="100%">Synchronization</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">PP</style></volume><pages><style face="normal" font="default" size="100%">1-1</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The large amount of space offered by personal cloud storage services (e.g., Dropbox and OneDrive), together with the possibility of synchronizing devices seamlessly, keep attracting customers to the cloud. Despite the high public interest, little information about system design and actual implications on performance is available when selecting a cloud storage service. Systematic benchmarks to assist in comparing services and understanding the effects of design choices are still lacking. This paper proposes a methodology to understand and benchmark personal cloud storage services. Our methodology unveils their architecture and capabilities. Moreover, by means of repeatable and customizable tests, it allows the measurement of performance metrics under different workloads. The effectiveness of the methodology is shown in a case study in which 11 services are compared under the same conditions. Our case study reveals interesting differences in design choices. Their implications are assessed in a series of benchmarks. Results show no clear winner, with all services having potential for improving performance. In some scenarios, the synchronization of the same files can take 20 times longer. In other cases, we observe a wastage of twice as much network capacity, questioning the design of some services. Our methodology and results are thus useful both as benchmarks and as guidelines for system design.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Enrico Bocchi</style></author><author><style face="normal" font="default" size="100%">Idilio Drago</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Personal Cloud Storage: Usage, Performance and Impact of Terminals </style></title><secondary-title><style face="normal" font="default" size="100%">4th IEEE International Conference on Cloud Networking (IEEE CloudNet 2015)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cloud storage</style></keyword><keyword><style  face="normal" font="default" size="100%">Monitoring</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ieee-cloudnet.org/program.html</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Niagara Falls, Canada</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Personal cloud storage services such as Dropbox and OneDrive are popular among Internet users. They help in sharing content and backing up data by relying on the cloud to store files. The rise of mobile terminals and the presence of new providers question whether the usage of cloud storage is evolving. This knowledge is essential to understand the workload these services need to handle, their performance, and implications. In this paper we present a comprehensive characterization of personal cloud storage services. Relying on traces collected for one month in an operational network, we show that users of each service present distinct behaviors. Dropbox is now threatened by competitors, with OneDrive and Google Drive reaching large market shares. However, the popularity of the latter services seems to be driven by their integration into Windows and Android. Indeed, around 50% of their users do not produce any workload. Considering performance, providers show distinct trade-offs, with bottlenecks that hardly allow users to fully exploit their access line bandwidth. Finally, usage of cloud services is now ordinary among mobile users, thanks to the automatic backup of pictures and media files.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hadrien Hours</style></author><author><style face="normal" font="default" size="100%">Ernst Biersack</style></author><author><style face="normal" font="default" size="100%">Patrick Loiseau</style></author><author><style face="normal" font="default" size="100%">Alessandro Finamore</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Study of the Impact of DNS Resolvers on Performance Using a Causal Approach</style></title><secondary-title><style face="normal" font="default" size="100%">Internet Teletraffic Congress</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">DNS</style></keyword><keyword><style  face="normal" font="default" size="100%">reasoner</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2015</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Ghent, Belgium</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">For a user to access any resource on the Internet, it is necessary to first locate a server hosting the requested resource. The Domain Name System service (DNS) represents the first step in this process, translating a human readable name, the resource host name, into an IP address. With the expansion of Content Distribution Networks (CDNs), the DNS service has seen its importance increase. In a CDN, objects are replicated on different servers to decrease the distance from the client to a server hosting the object that needs to be accessed. The DNS service should improve user experience by directing its demand to the optimal CDN server. While most of the Internet Service Providers (ISPs) offer a DNS service to their customers, it is now common to see clients using a public DNS service instead. This choice may have an impact on Web browsing performance. In this paper we study the impact of choosing one DNS server instead of another and we compare the performance of a large European ISP DNS service with the one of a public DNS service, Google DNS. We propose a causal approach to expose the structural dependencies of the different parameters impacted by the DNS service used and we show how to model these dependencies with a Bayesian network. This model allows us to explain and quantify the benefits obtained by clients using their ISP DNS service and to propose a solution to further improve their performance.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Arianna Rufini</style></author><author><style face="normal" font="default" size="100%">Edion Tego</style></author><author><style face="normal" font="default" size="100%">Francesco Matera</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bandwidth Measurements and Capacity Exploitation in Gigabit Passive Optical Networks</style></title><secondary-title><style face="normal" font="default" size="100%">Fotonica 2014</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">access capacity</style></keyword><keyword><style  face="normal" font="default" size="100%">GPON</style></keyword><keyword><style  face="normal" font="default" size="100%">QoE</style></keyword><keyword><style  face="normal" font="default" size="100%">QoS</style></keyword><keyword><style  face="normal" font="default" size="100%">Throughput</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2014</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We report an experimental investigation on the &lt;br /&gt;measurement of the available bandwidth for users in Gigabit &lt;br /&gt;Passive Optical Networks (GPON) and the limitations caused by &lt;br /&gt;the Internet protocols. We point out that the huge capacity &lt;br /&gt;offered by the GPON highlights the enormous differences that &lt;br /&gt;can be showed among the available and actually exploitable &lt;br /&gt;bandwidth in the case of TCP. In this ultrabroadband &lt;br /&gt;environment we also investigated on use of the UDP and of the&lt;br /&gt;multisession TCP. A correlation in terms of QoE is also reported.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Enrico Bocchi</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author><author><style face="normal" font="default" size="100%">Sofiane Sarni</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cloud Storage Service Benchmarking: Methodologies and Experimentations</style></title><secondary-title><style face="normal" font="default" size="100%">3rd IEEE International Conference on Cloud Networking (IEEE CloudNet 2014)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Amazon S3</style></keyword><keyword><style  face="normal" font="default" size="100%">Benchmarking</style></keyword><keyword><style  face="normal" font="default" size="100%">Cloud storage</style></keyword><keyword><style  face="normal" font="default" size="100%">Performance measurement</style></keyword><keyword><style  face="normal" font="default" size="100%">Web services</style></keyword><keyword><style  face="normal" font="default" size="100%">Windows Azure</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2014</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Luxembourg</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div class=&quot;page&quot; title=&quot;Page 1&quot;&gt;&lt;div class=&quot;layoutArea&quot;&gt;&lt;div class=&quot;column&quot;&gt;&lt;p&gt;&lt;span&gt;Data storage is one of today’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. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;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.&amp;nbsp;&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Naylor</style></author><author><style face="normal" font="default" size="100%">Alessandro Finamore</style></author><author><style face="normal" font="default" size="100%">Ilias Leontiadis</style></author><author><style face="normal" font="default" size="100%">Yan Grunenberger</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author><author><style face="normal" font="default" size="100%">Kostantina Papagiannaki</style></author><author><style face="normal" font="default" size="100%">Peter Steenkiste</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Cost of the “S” in HTTPS</style></title><secondary-title><style face="normal" font="default" size="100%">ACM Conference on emerging Networking EXperiments and Technologies (CoNEXT)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2014</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Umberto Manferdini</style></author><author><style face="normal" font="default" size="100%">Stefano Traverso</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author><author><style face="normal" font="default" size="100%">Edion Tego</style></author><author><style face="normal" font="default" size="100%">Francesco Matera</style></author><author><style face="normal" font="default" size="100%">Zied Ben Houidi</style></author><author><style face="normal" font="default" size="100%">Marco Milanesio</style></author><author><style face="normal" font="default" size="100%">Pietro Michiardi</style></author><author><style face="normal" font="default" size="100%">Dario Rossi</style></author><author><style face="normal" font="default" size="100%">D. Cicalese</style></author><author><style face="normal" font="default" size="100%">D. Joumblatt</style></author><author><style face="normal" font="default" size="100%">Jordan Augé</style></author><author><style face="normal" font="default" size="100%">Maurizio Dusi</style></author><author><style face="normal" font="default" size="100%">Sofia Nikitaki</style></author><author><style face="normal" font="default" size="100%">Mohamed Ahmed</style></author><author><style face="normal" font="default" size="100%">Ilias Leontiadis</style></author><author><style face="normal" font="default" size="100%">L. Baltrunas</style></author><author><style face="normal" font="default" size="100%">M. Varvello</style></author><author><style face="normal" font="default" size="100%">Pedro Casas</style></author><author><style face="normal" font="default" size="100%">Alessandro D'Alconzo</style></author><author><style face="normal" font="default" size="100%">Benoit Donnet</style></author><author><style face="normal" font="default" size="100%">W. Du</style></author><author><style face="normal" font="default" size="100%">Guy Leduc</style></author><author><style face="normal" font="default" size="100%">Y. Liao</style></author><author><style face="normal" font="default" size="100%">Alessandro Capello</style></author><author><style face="normal" font="default" size="100%">Fabrizio Invernizzi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%"> Cross-check of Analysis Modules and Reasoner Interactions</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">reasoner</style></keyword><keyword><style  face="normal" font="default" size="100%">WP4</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2014</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">D4.3</style></number><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Deliverable</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pedro Casas</style></author><author><style face="normal" font="default" size="100%">Alessandro D'Alconzo</style></author><author><style face="normal" font="default" size="100%">Maurizio Dusi</style></author><author><style face="normal" font="default" size="100%">Sofia Nikitaki</style></author><author><style face="normal" font="default" size="100%">Mohamed Ahmed</style></author><author><style face="normal" font="default" size="100%">Stefano Traverso</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author><author><style face="normal" font="default" size="100%">Daniele Apiletti</style></author><author><style face="normal" font="default" size="100%">Luigi Grimaudo</style></author><author><style face="normal" font="default" size="100%">Elena Baralis</style></author><author><style face="normal" font="default" size="100%">Dario Rossi</style></author><author><style face="normal" font="default" size="100%">D. Joumblatt</style></author><author><style face="normal" font="default" size="100%">Alessandro Capello</style></author><author><style face="normal" font="default" size="100%">M. D'Ambrosio</style></author><author><style face="normal" font="default" size="100%">Fabrizio Invernizzi</style></author><author><style face="normal" font="default" size="100%">M. Ullio</style></author><author><style face="normal" font="default" size="100%">Andrea Fregosi</style></author><author><style face="normal" font="default" size="100%">Eike Kowallik</style></author><author><style face="normal" font="default" size="100%">Stefano Raffaglio</style></author><author><style face="normal" font="default" size="100%">Andrea Sannino</style></author><author><style face="normal" font="default" size="100%">Marco Milanesio</style></author><author><style face="normal" font="default" size="100%">Edion Tego</style></author><author><style face="normal" font="default" size="100%">Francesco Matera</style></author><author><style face="normal" font="default" size="100%">Tivadar Szemethy</style></author><author><style face="normal" font="default" size="100%">Balazs Szabo</style></author><author><style face="normal" font="default" size="100%">L. Németh</style></author><author><style face="normal" font="default" size="100%">Zied Ben Houidi</style></author><author><style face="normal" font="default" size="100%">G. Dimopoulos</style></author><author><style face="normal" font="default" size="100%">Ilias Leontiadis</style></author><author><style face="normal" font="default" size="100%">Yan Grunenberger</style></author><author><style face="normal" font="default" size="100%">L. Baltrunas</style></author><author><style face="normal" font="default" size="100%">Michael Faath</style></author><author><style face="normal" font="default" size="100%">Rolf Winter</style></author><author><style face="normal" font="default" size="100%">Dimitri Papadimitriou</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design of the Reasoner</style></title><short-title><style face="normal" font="default" size="100%">D4.2</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">design</style></keyword><keyword><style  face="normal" font="default" size="100%">private deliverable</style></keyword><keyword><style  face="normal" font="default" size="100%">reasoner</style></keyword><keyword><style  face="normal" font="default" size="100%">WP4</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2014</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">D4.2</style></number><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">report</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ignacio Bermudez</style></author><author><style face="normal" font="default" size="100%">Stefano Traverso</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author><author><style face="normal" font="default" size="100%">Maurizio Munafo'</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Distributed Architecture for the Monitoring of Clouds and CDNs: Applications to Amazon AWS</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Network and Service Management</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Amazon</style></keyword><keyword><style  face="normal" font="default" size="100%">AWS</style></keyword><keyword><style  face="normal" font="default" size="100%">CDNs</style></keyword><keyword><style  face="normal" font="default" size="100%">Clouds</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">In press</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Clouds and CDNs are systems that tend to separate the content being requested by users from the physical servers capable of serving it. From the network point of view, monitoring and optimizing performance for the traffic they generate is a challenging task, given the same resource can be located in multiple places, which can in turn change at any time. The first step in understanding Cloud and CDN systems is thus the engineering of a monitoring platform. In this paper, we propose a novel solution which combines passive and active measurements, and whose workflow has been tailored to specifically characterize the traffic generated by Cloud and CDN infrastructures. We validate our platform by performing a longitudinal characterization of the very well known Cloud and CDN infrastructure provider Amazon Web Services (AWS). By observing the traffic generated by more than 50,000 Internet users of an Italian ISP, we explore the EC2, S3 and CloudFront AWS services, unveiling their infrastructure, the pervasiveness of web-services they host, and their traffic allocation policies as seen from our vantage points. Most importantly, we observe their evolution over a two- year long period. The solution provided in this paper can be of interest for i) developers aiming at building measurement tools for Cloud Infrastructure Providers, ii) developers interested in failure and anomaly detection systems, and iii) third-party SLA certificators who can design systems to independently monitor performance. At last, we believe the results about AWS presented in this paper are interesting as they are among the first to unveil properties of AWS as seen from the operator point of view.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stefano Traverso</style></author><author><style face="normal" font="default" size="100%">Edion Tego</style></author><author><style face="normal" font="default" size="100%">Eike Kowallik</style></author><author><style face="normal" font="default" size="100%">Stefano Raffaglio</style></author><author><style face="normal" font="default" size="100%">Andrea Fregosi</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author><author><style face="normal" font="default" size="100%">Francesco Matera</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exploiting Hybrid Measurements for Network Troubleshooting</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Networks</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Hybrid measurements</style></keyword><keyword><style  face="normal" font="default" size="100%">measurement analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">WP2</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2014</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Funchal, PT</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Network measurements are a fundamental pillar to understand network performance and perform root cause analysis in case of problems. Traditionally, either active or passive measurements are considered. While active measurements allow to know exactly the workload injected by the application into the network, the passive measurements can offer a more detailed view of transport and network layer impacts. In this paper, we present a hybrid approach in which active throughput measurements are regularly run while a passive measurement tool monitors the generated packets. This allows us to correlate the application layer measurements obtained by the active tool with the more detailed view offered by the passive monitor.
The proposed methodology has been implemented following the mPlane reference architecture, tools have been installed in the Fastweb network, and we collect measurements for more than three months. We report then a subset of results that show the benefits obtained when correlating active and passive measurements. Among results, we pinpoint cases of congestion, of ADSL misconfiguration, and of modem issues that impair throughput obtained by the users.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zied Ben-Houidi</style></author><author><style face="normal" font="default" size="100%">Giuseppe Scavo</style></author><author><style face="normal" font="default" size="100%">Samir Ghamri-Doudane</style></author><author><style face="normal" font="default" size="100%">Alessandro Finamore</style></author><author><style face="normal" font="default" size="100%">Stefano Traverso</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gold mining in a River of Internet Content Traffic</style></title><secondary-title><style face="normal" font="default" size="100%">6th International Workshop on Traffic Monitoring and Analysis, TMA</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Content mining</style></keyword><keyword><style  face="normal" font="default" size="100%">HTTP Traffic</style></keyword><keyword><style  face="normal" font="default" size="100%">URL extraction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">04/2014</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">London</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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 “user-URLs”, 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.
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Arian Bär</style></author><author><style face="normal" font="default" size="100%">Alessandro Finamore</style></author><author><style face="normal" font="default" size="100%">Pedro Casas</style></author><author><style face="normal" font="default" size="100%">Lukasz Golab</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Large-Scale Network Traffic Monitoring with DBStream, a System for Rolling Big Data Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Big Data, IEEE BigData</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Big Data Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Data Stream Processing</style></keyword><keyword><style  face="normal" font="default" size="100%">network data analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">System Performance</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2014</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Washington D.C., USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brian Trammell</style></author><author><style face="normal" font="default" size="100%">Pedro Casas</style></author><author><style face="normal" font="default" size="100%">Dario Rossi</style></author><author><style face="normal" font="default" size="100%">Arian Bär</style></author><author><style face="normal" font="default" size="100%">Zied Ben-Houidi</style></author><author><style face="normal" font="default" size="100%">Ilias Leontiadis</style></author><author><style face="normal" font="default" size="100%">Tivadar Szemethy</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">mPlane: an Intelligent Measurement Plane for the Internet</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Communications Magazine, Special Issue on Monitoring and Troubleshooting Multi-domain Networks using Measurement Federations</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2014</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">42</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">5</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Arianna Rufini</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author><author><style face="normal" font="default" size="100%">Edion Tego</style></author><author><style face="normal" font="default" size="100%">Francesco Matera</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multilevel Bandwidth Measurements and Capacity Exploitation in Gigabit Passive Optical Networks</style></title><secondary-title><style face="normal" font="default" size="100%">IET Communications</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Fiber Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">GPON</style></keyword><keyword><style  face="normal" font="default" size="100%">Quality of Service</style></keyword><keyword><style  face="normal" font="default" size="100%">TCP</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2014</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6980492</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">8</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;JOCNAbstract&quot;&gt;&lt;span&gt;We report an experimental investigation on the measurement of the available bandwidth for the users in Gigabit Passive Optical Networks (GPON) and the limitations caused by the Internet protocols, and TCP in particular. We point out that the huge capacity offered by the GPON highlights the enormous differences that can be showed among the available and actually exploitable bandwidth. In fact, while the physical layer capacity can reach value of 100 Mb/s and more, the bandwidth at disposal of the user (i.e. either throughput at transport layer or goodput at application layer) can be much lower when applications and services based on TCP protocol are considered. In the context of Service Level Agreements (SLA) verification, we show how to simultaneously measure throughput and line capacity by offering a method to verify multilayer SLA. We also show how it is possible to better to exploit the physical layer capacity by adopting multiple TCP connections to avoid the bottleneck of a single connection.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">18</style></issue><section><style face="normal" font="default" size="100%">3357</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">S. Colabrese</style></author><author><style face="normal" font="default" size="100%">D Rossi</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Scalable accurate consolidation of passively measured statistical data</style></title><secondary-title><style face="normal" font="default" size="100%">Passive and Active Measurement (PAM), Extended Abstract</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">March</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://perso.telecom-paristech.fr/~drossi/paper/rossi14pam-a.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Los Angeles, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Luigi Grimaudo</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author><author><style face="normal" font="default" size="100%">Elena Baralis</style></author><author><style face="normal" font="default" size="100%">Ram Keralapura</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SeLeCT: Self-Learning Classifier for Internet Traffic</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Network and Service Management</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">self-seeding</style></keyword><keyword><style  face="normal" font="default" size="100%">Traffic Classification</style></keyword><keyword><style  face="normal" font="default" size="100%">unsupervised machine learning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2014</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">11</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Network visibility is a critical part of traffic engineering, network management, and security. The most popular&amp;nbsp;current solutions - Deep Packet Inspection (DPI) and statistical&amp;nbsp;classification, deeply rely on the availability of a training set.&amp;nbsp;Besides the cumbersome need to regularly update the signatures,&amp;nbsp;their visibility is limited to classes the classifier has been trained&amp;nbsp;for. Unsupervised algorithms have been envisioned as a viable&amp;nbsp;alternative to automatically identify classes of traffic. However,&amp;nbsp;the accuracy achieved so far does not allow to use them for traffic&amp;nbsp;classification in practical scenario.&lt;/p&gt;&lt;p&gt;To address the above issues, we propose SeLeCT, a Self-Learning Classifier for Internet Traffic. It uses unsupervised algorithms along with an adaptive seeding approach to automatically&amp;nbsp;let classes of traffic emerge, being identified and labeled. Unlike&amp;nbsp;traditional classifiers, it requires neither a-priori knowledge of&amp;nbsp;signatures nor a training set to extract the signatures. Instead,&amp;nbsp;SeLeCT automatically groups flows into pure (or homogeneous)&amp;nbsp;clusters using simple statistical features. SeLeCT simplifies label&amp;nbsp;assignment (which is still based on some manual intervention) so&amp;nbsp;that proper class labels can be easily discovered. Furthermore,&amp;nbsp;SeLeCT uses an iterative seeding approach to boost its ability to&amp;nbsp;cope with new protocols and applications.&lt;/p&gt;&lt;p&gt;We evaluate the performance of SeLeCT using traffic traces&amp;nbsp;collected in different years from various ISPs located in 3&amp;nbsp;different continents. Our experiments show that SeLeCT achieves&amp;nbsp;excellent precision and recall, with overall accuracy close to 98%.&amp;nbsp;Unlike state-of-art classifiers, the biggest advantage of SeLeCT&amp;nbsp;is its ability to discover new protocols and applications in an&amp;nbsp;almost automated fashion.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><section><style face="normal" font="default" size="100%">144</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pedro Casas</style></author><author><style face="normal" font="default" size="100%">Pierdomenico Fiadino</style></author><author><style face="normal" font="default" size="100%">Arian Bär</style></author><author><style face="normal" font="default" size="100%">Alessandro D'Alconzo</style></author><author><style face="normal" font="default" size="100%">Alessandro Finamore</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">YouTube All Around: Characterizing YouTube from Mobile and Fixed-line Network Vantage Points</style></title><secondary-title><style face="normal" font="default" size="100%">EuCNC</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2014</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Bologna, IT</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Idilio Drago</style></author><author><style face="normal" font="default" size="100%">Enrico Bocchi</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author><author><style face="normal" font="default" size="100%">Herman Slatman</style></author><author><style face="normal" font="default" size="100%">Aiko Pras</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Benchmarking Personal Cloud Storage</style></title><secondary-title><style face="normal" font="default" size="100%">Internet Measurement Conference - IMC</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Active Measurements</style></keyword><keyword><style  face="normal" font="default" size="100%">Personal Cloud Storage</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2013</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.simpleweb.org/wiki/Cloud_benchmarks</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">Barcelona (ES)</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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 – most of all – 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.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ignacio Nicolas Bermudez</style></author><author><style face="normal" font="default" size="100%">Stefano Traverso</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author><author><style face="normal" font="default" size="100%">Maurizio M Munafo'</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exploring the Cloud from Passive Measurements: the Amazon AWS case</style></title><secondary-title><style face="normal" font="default" size="100%">The 32nd Annual IEEE International Conference on Computer Communications (INFOCOM'2013)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pub-location><style face="normal" font="default" size="100%">Turin, Italy</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Cloud Providers are nowadays the most popular way to quickly deploy new services on the Internet. Understanding mechanisms currently adopted in cloud design is fundamental to identify possible bottlenecks, to optimize performance, and to design more efficient platforms. This paper presents a characterization of Amazon's Web Services (AWS), the most prominent cloud provider that offers computing, storage, and content delivery platforms. Leveraging passive measurements collected from several vantage points in Italy for several months, we explore the EC2, S3 and CloudFront AWS services to unveil their infrastructure, the pervasiveness of content they host, and their traffic allocation policies. Measurements reveal that most of the content residing on EC2 and S3 is served by one single Amazon datacenter located in Virginia despite it appears to be the worst performing one for Italian users. This causes traffic to take long and expensive paths in the network. Since no automatic migration and load-balancing policies are offered by AWS among different locations, content is exposed to outages, as we were able to observe in our data. The CloudFront CDN, on the contrary, shows much better performance thanks to the effective cache selection policy that serves 98% of the traffic from the nearest available cache. CloudFront exhibits also dynamic load-balancing policies, in contrast to the static allocation of instances on EC2 and S3. Information presented in this paper will be useful for developers aiming at entrusting AWS to deploy their contents, and for researchers willing to improve cloud design.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alessandro Capello</style></author><author><style face="normal" font="default" size="100%">Fabrizio Invernizzi</style></author><author><style face="normal" font="default" size="100%">Omar Jabr</style></author><author><style face="normal" font="default" size="100%">Dimitri Papadimitriou</style></author><author><style face="normal" font="default" size="100%">Dario Rossi</style></author><author><style face="normal" font="default" size="100%">YiXi Gong</style></author><author><style face="normal" font="default" size="100%">Brian Trammell</style></author><author><style face="normal" font="default" size="100%">Marco Milanesio</style></author><author><style face="normal" font="default" size="100%">Ernst Biersack</style></author><author><style face="normal" font="default" size="100%">Rolf Winter</style></author><author><style face="normal" font="default" size="100%">Francesco Matera</style></author><author><style face="normal" font="default" size="100%">Arianna Rufini</style></author><author><style face="normal" font="default" size="100%">Edion Tego</style></author><author><style face="normal" font="default" size="100%">Maurizio Dusi</style></author><author><style face="normal" font="default" size="100%">Balazs Szabo</style></author><author><style face="normal" font="default" size="100%">Tivadar Szemethy</style></author><author><style face="normal" font="default" size="100%">Alessandro Finamore</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author><author><style face="normal" font="default" size="100%">Ilias Leontiadis</style></author><author><style face="normal" font="default" size="100%">Benoit Donnet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">First Data Collection Track Record</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">data sets</style></keyword><keyword><style  face="normal" font="default" size="100%">integration</style></keyword><keyword><style  face="normal" font="default" size="100%">measurement systems</style></keyword><keyword><style  face="normal" font="default" size="100%">scenarios</style></keyword><keyword><style  face="normal" font="default" size="100%">use cases</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2013</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">D5.1</style></number><publisher><style face="normal" font="default" size="100%">mPlane Consortium</style></publisher><pub-location><style face="normal" font="default" size="100%">Torino</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Private Deliverable</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brian Trammell</style></author><author><style face="normal" font="default" size="100%">Alessandro Finamore</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Measurement-Centered Approach to Latency Reduction</style></title><secondary-title><style face="normal" font="default" size="100%">ISOC Workshop on Reducing Internet Latency</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2013</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.internetsociety.org/latency2013</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">London, England</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Position Paper</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brian Trammell</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author><author><style face="normal" font="default" size="100%">Alessandro Finamore</style></author><author><style face="normal" font="default" size="100%">Stefano Traverso</style></author><author><style face="normal" font="default" size="100%">Tivadar Szemethy</style></author><author><style face="normal" font="default" size="100%">Balazs Szabo</style></author><author><style face="normal" font="default" size="100%">D Rossi</style></author><author><style face="normal" font="default" size="100%">Benoit Donnet</style></author><author><style face="normal" font="default" size="100%">Fabrizio Invernizzi</style></author><author><style face="normal" font="default" size="100%">Dimitri Papadimitriou</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">mPlane Architecture Speciﬁcation</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">architecture</style></keyword><keyword><style  face="normal" font="default" size="100%">measurement</style></keyword><keyword><style  face="normal" font="default" size="100%">platform</style></keyword><keyword><style  face="normal" font="default" size="100%">scenario</style></keyword><keyword><style  face="normal" font="default" size="100%">use case</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2013</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">D1.3</style></number><publisher><style face="normal" font="default" size="100%">mPlane Consortium</style></publisher><pub-location><style face="normal" font="default" size="100%">Torino</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Public Deliverable</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dimitri Papadimitriou</style></author><author><style face="normal" font="default" size="100%">Dario Rossi</style></author><author><style face="normal" font="default" size="100%">YiXi Gong</style></author><author><style face="normal" font="default" size="100%">Brian Trammell</style></author><author><style face="normal" font="default" size="100%">Marco Milanesio</style></author><author><style face="normal" font="default" size="100%">Ernst Biersack</style></author><author><style face="normal" font="default" size="100%">Rolf Winter</style></author><author><style face="normal" font="default" size="100%">Francesco Matera</style></author><author><style face="normal" font="default" size="100%">Maurizio Dusi</style></author><author><style face="normal" font="default" size="100%">Balazs Szabo</style></author><author><style face="normal" font="default" size="100%">Tivadar Szemethy</style></author><author><style face="normal" font="default" size="100%">Alessandro Finamore</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author><author><style face="normal" font="default" size="100%">Alessandro Capello</style></author><author><style face="normal" font="default" size="100%">Fabio Invernizzi</style></author><author><style face="normal" font="default" size="100%">Omar Jabr</style></author><author><style face="normal" font="default" size="100%">Ilias Leontiadis</style></author><author><style face="normal" font="default" size="100%">Benoit Donnet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Selection of Existing Probes and Datasets</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">active probes</style></keyword><keyword><style  face="normal" font="default" size="100%">existing probes</style></keyword><keyword><style  face="normal" font="default" size="100%">passive probes</style></keyword><keyword><style  face="normal" font="default" size="100%">probes</style></keyword><keyword><style  face="normal" font="default" size="100%">proxy probes</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2013</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">D2.1</style></number><publisher><style face="normal" font="default" size="100%">mPlane Consortium</style></publisher><pub-location><style face="normal" font="default" size="100%">Torino</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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'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</style></abstract><work-type><style face="normal" font="default" size="100%">Public Deliverable</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Luigi Grimaudo</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author><author><style face="normal" font="default" size="100%">Elena Baralis</style></author><author><style face="normal" font="default" size="100%">Ram Keralapura</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Self-Learning Classifier for Internet Traffic</style></title><secondary-title><style face="normal" font="default" size="100%">The 5th IEEE International Traffic Monitoring and Analysis Workshop (TMA 2013)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Network visibility is a critical part of traffic engineering, network management, and security. Recently, unsupervised algorithms have been envisioned as a viable alternative&amp;nbsp;to automatically identify classes of traffic. However, the accuracy&amp;nbsp;achieved so far does not allow to use them for traffic classification&amp;nbsp;in practical scenario.&lt;br /&gt;In this paper, we propose SeLeCT, a Self-Learning Classifier&amp;nbsp;for Internet traffic. It uses unsupervised algorithms along with&amp;nbsp;an adaptive learning approach to automatically let classes of&amp;nbsp;traffic emerge, being identified and (easily) labeled. SeLeCT&amp;nbsp;automatically groups flows into pure (or homogeneous) clusters&amp;nbsp;using alternating simple clustering and filtering phases to remove&amp;nbsp;outliers. SeLeCT uses an adaptive learning approach to boost its&amp;nbsp;ability to spot new protocols and applications. Finally, SeLeCT&amp;nbsp;also simplifies label assignment (which is still based on some&amp;nbsp;manual intervention) so that proper class labels can be easily&amp;nbsp;discovered.&lt;br /&gt;We evaluate the performance of SeLeCT using traffic traces&amp;nbsp;collected in different years from various ISPs located in 3&amp;nbsp;different continents. Our experiments show that SeLeCT achieves&amp;nbsp;overall accuracy close to 98%. Unlike state-of-art classifiers, the&amp;nbsp;biggest advantage of SeLeCT is its ability to help discovering&amp;nbsp;new protocols and applications in an almost automated fashion.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ignacio Nicolas Bermudez</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author><author><style face="normal" font="default" size="100%">Maurizio M Munafo'</style></author><author><style face="normal" font="default" size="100%">Ram Keralapura</style></author><author><style face="normal" font="default" size="100%">Antonio Nucci</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">DNS to the rescue: Discerning Content and Services in a Tangled Web</style></title><secondary-title><style face="normal" font="default" size="100%">Internet Measurement Conference 2012</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">DNS</style></keyword><keyword><style  face="normal" font="default" size="100%">mPlane</style></keyword><keyword><style  face="normal" font="default" size="100%">passive measurement</style></keyword><keyword><style  face="normal" font="default" size="100%">WP2</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dl.acm.org/citation.cfm?id=2398776.2398819&amp;coll=DL&amp;dl=GUIDE&amp;CFID=225051145&amp;CFTOKEN=42401286</style></url></web-urls></urls><edition><style face="normal" font="default" size="100%">ACM</style></edition><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">Boston, MA</style></pub-location><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">413-426</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4503-1705-4</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div class=&quot;page&quot; title=&quot;Page 1&quot;&gt;&lt;div class=&quot;layoutArea&quot;&gt;&lt;div class=&quot;column&quot;&gt;&lt;p&gt;&lt;span&gt;A careful perusal of the Internet evolution reveals two major trends - explosion of cloud-based services and video stream- ing applications. In both of the above cases, the owner (e.g., CNN, YouTube, or Zynga) of the content and the organiza- tion serving it (e.g., Akamai, Limelight, or Amazon EC2) are decoupled, thus making it harder to understand the associ- ation between the content, owner, and the host where the content resides. This has created a tangled world wide web that is very hard to unwind, impairing ISPs’ and network administrators’ capabilities to control the traffic flowing in their networks. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;In this paper, we present DN-Hunter, a system that lever- ages the information provided by DNS traffic to discern the tangle. Parsing through DNS queries, DN-Hunter tags traf- fic flows with the associated domain name. This association has several applications and reveals a large amount of use- ful information: (&lt;/span&gt;&lt;span&gt;i&lt;/span&gt;&lt;span&gt;) Provides a fine-grained traffic visibility even when the traffic is encrypted (i.e., TLS/SSL flows), thus enabling more effective policy controls, (&lt;/span&gt;&lt;span&gt;ii&lt;/span&gt;&lt;span&gt;) Identifies flows even before the flows begin, thus providing superior net- work management capabilities to administrators, (&lt;/span&gt;&lt;span&gt;iii&lt;/span&gt;&lt;span&gt;) Un- derstand and track (over time) different CDNs and cloud providers that host content for a particular resource, (&lt;/span&gt;&lt;span&gt;iv&lt;/span&gt;&lt;span&gt;) Discern all the services/content hosted by a given CDN or cloud provider in a particular geography and time interval, and (&lt;/span&gt;&lt;span&gt;v&lt;/span&gt;&lt;span&gt;) Provides insights into all applications/services run- ning on any given layer-4 port number. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;We conduct extensive experimental analysis and show re- sults from real traffic traces (including FTTH and 4G ISPs) that support our hypothesis. Simply put, the information provided by DNS traffic is one of the key components re- quired for understanding the tangled web, and bringing the ability to effectively manage network traffic back to the op- erators.&amp;nbsp;&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;</style></abstract><num-vols><style face="normal" font="default" size="100%">1</style></num-vols></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Idilio Drago</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author><author><style face="normal" font="default" size="100%">Maurizio M Munafo'</style></author><author><style face="normal" font="default" size="100%">Anna Sperotto</style></author><author><style face="normal" font="default" size="100%">Ramin Sadre</style></author><author><style face="normal" font="default" size="100%">Aiko Pras</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Inside Dropbox: Understanding Personal Cloud Storage Services</style></title><secondary-title><style face="normal" font="default" size="100%">Internet Measurement Conference - IMC</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Dropbox</style></keyword><keyword><style  face="normal" font="default" size="100%">passive measurement</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dl.acm.org/citation.cfm?id=2398776.2398827&amp;coll=DL&amp;dl=GUIDE&amp;CFID=225051145&amp;CFTOKEN=42401286</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">Boston, MA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div class=&quot;page&quot; title=&quot;Page 1&quot;&gt;&lt;div class=&quot;layoutArea&quot;&gt;&lt;div class=&quot;column&quot;&gt;&lt;p&gt;&lt;span&gt;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. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;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. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;All measurements used in our analyses are publicly avail- able in anonymized form at the SimpleWeb trace repository: &lt;/span&gt;&lt;span&gt;&lt;a href=&quot;http://traces.simpleweb.org/dropbox/&quot; title=&quot;Linkification: http://traces.simpleweb.org/dropbox/&quot; class=&quot;linkification-ext&quot; style=&quot;color: #006620;&quot;&gt;http://traces.simpleweb.org/dropbox/&lt;/a&gt;&amp;nbsp;&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alessandro Finamore</style></author><author><style face="normal" font="default" size="100%">Vinicius Gehlen</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author><author><style face="normal" font="default" size="100%">Maurizio M Munafo'</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The need for an intelligent measurement plane: The example of time-variant CDN policies</style></title><secondary-title><style face="normal" font="default" size="100%">Telecommunications Network Strategy and Planning Symposium (NETWORKS), 2012 XVth International </style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Facebook</style></keyword><keyword><style  face="normal" font="default" size="100%">Google</style></keyword><keyword><style  face="normal" font="default" size="100%">Monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">Organizations</style></keyword><keyword><style  face="normal" font="default" size="100%">Servers</style></keyword><keyword><style  face="normal" font="default" size="100%">Streaming media</style></keyword><keyword><style  face="normal" font="default" size="100%">Throughput</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2012</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">1 - 6 </style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper we characterize how web-based services are delivered by large organizations in today's Internet. Taking advantage oftwo week-long data sets separated in time by 10 months and reporting the web activity of more than 10,000 ADSL residential customers, we identify the services offered by large organizations like Google, Akamai and Amazon. We then compare theevolution of both policies used to serve requests, and the infrastructure they use to match the users' demand. Results depict anovercrowded scenario in constant evolution. Big-players are more and more responsible for the majority of the volume and a plethora of other organizations offering similar or more specific services through different CDNs and traffic policies. Unfortunately, no standard tools and methodologies are available to capture and expose the hidden properties of this in constant evolution picture. A deeper understanding of such dynamics is however fundamental to improve the performance of current and future Internet. To this extend, we claim the need for a Internet-wide, standard, flexible and intelligent measurement plane to be added tothe current Internet infrastructure.</style></abstract></record></records></xml>