<?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%">Liao, Yongjun</style></author><author><style face="normal" font="default" size="100%">Du, Wei</style></author><author><style face="normal" font="default" size="100%">Leduc, Guy</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Lightweight Network Proximity Service Based On Neighborhood Models</style></title><secondary-title><style face="normal" font="default" size="100%">22nd IEEE Symposium on Communications and Vehicular Technology in the Benelux  (SCVT)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2015</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;p&gt;This paper proposes a network proximity service&amp;nbsp;based on the neighborhood models used in recommender systems.&amp;nbsp;Unlike previous approaches, our service infers network proximity&amp;nbsp;without trying to recover the latency between network nodes. By&amp;nbsp;asking each node to probe a number of landmark nodes which&amp;nbsp;can be servers at Google, Yahoo and Facebook, etc., a simple&amp;nbsp;proximity measure is computed and allows the direct ranking&amp;nbsp;and rating of network nodes by their proximity to a target node.&amp;nbsp;The service is thus lightweight and can be easily deployed in&amp;nbsp;e.g. P2P and CDN applications. Simulations on existing datasets&amp;nbsp;and experiments with a deployment over PlanetLab showed&amp;nbsp;that our service achieves an accurate proximity inference that&amp;nbsp;is comparable to state-of-the-art latency prediction approaches,&amp;nbsp;while being much simpler.&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%">Du, Wei</style></author><author><style face="normal" font="default" size="100%">Liao, Yongjun</style></author><author><style face="normal" font="default" size="100%">Tao, Narisu</style></author><author><style face="normal" font="default" size="100%">Geurts, Pierre</style></author><author><style face="normal" font="default" size="100%">Fu, Xiaoming</style></author><author><style face="normal" font="default" size="100%">Leduc, Guy</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Rating Network Paths for Locality-Aware Overlay Construction and Routing</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE/ACM Transactions on Networking</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">matrix factorization</style></keyword><keyword><style  face="normal" font="default" size="100%">network inference</style></keyword><keyword><style  face="normal" font="default" size="100%">rating-based network measurement</style></keyword><keyword><style  face="normal" font="default" size="100%">recommender system</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><volume><style face="normal" font="default" size="100%">23</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper investigates the rating of network paths,&amp;nbsp;i.e. acquiring quantized measures of path properties such as&amp;nbsp;round-trip time and available bandwidth. Comparing to finegrained&amp;nbsp;measurements, coarse-grained ratings are appealing in&amp;nbsp;that they are not only informative but also cheap to obtain.&lt;/p&gt;&lt;p&gt;Motivated by this insight, we firstly address the scalable&amp;nbsp;acquisition of path ratings by statistical inference. By observing&amp;nbsp;similarities to recommender systems, we examine the applicability&amp;nbsp;of solutions to recommender system and show that our&amp;nbsp;inference problem can be solved by a class of matrix factorization&amp;nbsp;techniques. A technical contribution is an active and progressive&amp;nbsp;inference framework that not only improves the accuracy by&amp;nbsp;selectively measuring more informative paths but also speeds&amp;nbsp;up the convergence for available bandwidth by incorporating its&amp;nbsp;measurement methodology.&lt;/p&gt;&lt;p&gt;Then, we investigate the usability of rating-based network&amp;nbsp;measurement and inference in applications. A case study is&amp;nbsp;performed on whether locality awareness can be achieved for&amp;nbsp;overlay networks of Pastry and BitTorrent using inferred ratings.&lt;/p&gt;&lt;p&gt;We show that such coarse-grained knowledge can improve the&amp;nbsp;performance of peer selection and that finer granularities do not&amp;nbsp;always lead to larger improvements.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><section><style face="normal" font="default" size="100%">1661</style></section></record></records></xml>