<?xml version="1.0" encoding="UTF-8"?><xml><records><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%">Fiadino, Pierdomenico</style></author><author><style face="normal" font="default" size="100%">Casas, Pedro</style></author><author><style face="normal" font="default" size="100%">Schiavone, Mirko</style></author><author><style face="normal" font="default" size="100%">D'Alconzo, Alessandro</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Online Social Networks anatomy: On the analysis of Facebook and WhatsApp in cellular networks</style></title><secondary-title><style face="normal" font="default" size="100%">IFIP Networking Conference (IFIP Networking), 2015</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cellular Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Content Delivery Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Europe</style></keyword><keyword><style  face="normal" font="default" size="100%">Facebook</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">IP networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Network Measurements</style></keyword><keyword><style  face="normal" font="default" size="100%">Online Social Networks</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%">WhatsApp</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%">May</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>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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Casas, Pedro</style></author><author><style face="normal" font="default" size="100%">D'Alconzo, Alessandro</style></author><author><style face="normal" font="default" size="100%">Fiadino, Pierdomenico</style></author><author><style face="normal" font="default" size="100%">Bär, Arian</style></author><author><style face="normal" font="default" size="100%">Finamore, Alessandro</style></author><author><style face="normal" font="default" size="100%">Zseby, Tanja</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">When YouTube Does not Work - Analysis of QoE-Relevant Degradation in Google CDN Traffic</style></title><secondary-title><style face="normal" font="default" size="100%">Network and Service Management, IEEE Transactions on</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CDN distributed services</style></keyword><keyword><style  face="normal" font="default" size="100%">CDN server selection strategies</style></keyword><keyword><style  face="normal" font="default" size="100%">client-server systems</style></keyword><keyword><style  face="normal" font="default" size="100%">content delivery network</style></keyword><keyword><style  face="normal" font="default" size="100%">Content Delivery Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Degradation</style></keyword><keyword><style  face="normal" font="default" size="100%">dynamic approach</style></keyword><keyword><style  face="normal" font="default" size="100%">dynamic server selection strategies</style></keyword><keyword><style  face="normal" font="default" size="100%">end-user QoE</style></keyword><keyword><style  face="normal" font="default" size="100%">end-user quality of experience</style></keyword><keyword><style  face="normal" font="default" size="100%">European ISP</style></keyword><keyword><style  face="normal" font="default" size="100%">Google</style></keyword><keyword><style  face="normal" font="default" size="100%">Google CDN traffic</style></keyword><keyword><style  face="normal" font="default" size="100%">Google server selection strategies</style></keyword><keyword><style  face="normal" font="default" size="100%">IP networks</style></keyword><keyword><style  face="normal" font="default" size="100%">iterative structured process</style></keyword><keyword><style  face="normal" font="default" size="100%">load reduction</style></keyword><keyword><style  face="normal" font="default" size="100%">QoE-relevant anomaly characterization</style></keyword><keyword><style  face="normal" font="default" size="100%">QoE-relevant anomaly detection</style></keyword><keyword><style  face="normal" font="default" size="100%">QoE-relevant anomaly diagnosis</style></keyword><keyword><style  face="normal" font="default" size="100%">QoE-relevant degradation</style></keyword><keyword><style  face="normal" font="default" size="100%">Quality of Experience</style></keyword><keyword><style  face="normal" font="default" size="100%">Servers</style></keyword><keyword><style  face="normal" font="default" size="100%">social networking (online)</style></keyword><keyword><style  face="normal" font="default" size="100%">statistical analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">statistical analysis methodologies</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical Data Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">telecommunication traffic</style></keyword><keyword><style  face="normal" font="default" size="100%">Traffic Monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">Videos</style></keyword><keyword><style  face="normal" font="default" size="100%">watching experience improvement</style></keyword><keyword><style  face="normal" font="default" size="100%">YouTube</style></keyword><keyword><style  face="normal" font="default" size="100%">YouTube flow trace collection</style></keyword><keyword><style  face="normal" font="default" size="100%">YouTube QoE-relevant degradation</style></keyword><keyword><style  face="normal" font="default" size="100%">YouTube videos</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%">Dec</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">441-457</style></pages><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%">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>