<?xml version="1.0" encoding="UTF-8"?><xml><records><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%">Pietro Michiardi</style></author><author><style face="normal" font="default" size="100%">Antonio Barbuzzi</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%">Daniele Apiletti</style></author><author><style face="normal" font="default" size="100%">Elena Baralis</style></author><author><style face="normal" font="default" size="100%">Tania Cerquitelli</style></author><author><style face="normal" font="default" size="100%">Silvia Chiusano</style></author><author><style face="normal" font="default" size="100%">Luigi Grimaudo</style></author><author><style face="normal" font="default" size="100%">A. Rufini</style></author><author><style face="normal" font="default" size="100%">Francesco Matera</style></author><author><style face="normal" font="default" size="100%">A. Valentii</style></author><author><style face="normal" font="default" size="100%">Maurizio Dusi</style></author><author><style face="normal" font="default" size="100%">Mohamed Ahmed</style></author><author><style face="normal" font="default" size="100%">Tivadar Szemethy</style></author><author><style face="normal" font="default" size="100%">L. Németh</style></author><author><style face="normal" font="default" size="100%">R. Szalay</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%">P. Casas</style></author><author><style face="normal" font="default" size="100%">Alessandro D’Alconzo</style></author><author><style face="normal" font="default" size="100%">A Bär</style></author><author><style face="normal" font="default" size="100%">D Rossi</style></author><author><style face="normal" font="default" size="100%">YiXi Gong</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Basic Network Data Analysis</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">big data</style></keyword><keyword><style  face="normal" font="default" size="100%">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%">05/2013</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">D3.1</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%">&lt;p&gt;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.&lt;br /&gt;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.&lt;br /&gt;For each algorithm and use case, the input data and the initial state, the computation to run and the output produced are described.&lt;/p&gt;</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>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%">Zied Ben-Houidi</style></author><author><style face="normal" font="default" size="100%">Samir Ghamri-Doudane</style></author><author><style face="normal" font="default" size="100%">D Rossi</style></author><author><style face="normal" font="default" size="100%">M. Milanesio</style></author><author><style face="normal" font="default" size="100%">P. Casas</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%">Maurizio Dusi</style></author><author><style face="normal" font="default" size="100%">Tivadar Szemethy</style></author><author><style face="normal" font="default" size="100%">L. Máthé</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%">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%">Benoit Donnet</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></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design of Analysis Modules</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">analysis</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%">D4.1</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><isbn><style face="normal" font="default" size="100%">D4.1</style></isbn><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>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%">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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stefano Traverso</style></author><author><style face="normal" font="default" size="100%">Mohamed Ahmed</style></author><author><style face="normal" font="default" size="100%">Michele Garetto</style></author><author><style face="normal" font="default" size="100%">Paolo Giaccone</style></author><author><style face="normal" font="default" size="100%">Emilio Leonardi</style></author><author><style face="normal" font="default" size="100%">Saverio Niccolini</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Temporal locality in today's content caching: why it matters and how to model it</style></title><secondary-title><style face="normal" font="default" size="100%">ACM SIGCOMM Computer Communication Review</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%">10/2013</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">43</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The dimensioning of caching systems represents a difficult task in the design of infrastructures for content distribution in the current Internet. This paper addresses the problem of defining a realistic arrival process for the content requests generated by users, due its critical importance for both analytical and simulative evaluations of the performance of caching systems. First, with the aid of YouTube traces collected inside operational residential networks, we identify the characteristics of real traffic that need to be considered or can be safely neglected in order to accurately predict the performance of a cache. Second, we propose a new parsimonious traffic model, named the Shot Noise Model (SNM), that enables users to natively capture the dynamics of content popularity, whilst still being suf- ficiently simple to be employed effectively for both analytical and scalable simulative studies of caching systems. Finally, our results show that the SNM presents a much better solution to account for the temporal locality observed in real traffic compared to existing approaches.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><section><style face="normal" font="default" size="100%">5</style></section></record></records></xml>