<?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%">Luca Cittadini</style></author><author><style face="normal" font="default" size="100%">Stefano Vissichio</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%">On the Quality of BGP Route Collectors for iBGP Policy Inference</style></title><secondary-title><style face="normal" font="default" size="100%">IFIP Networking</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">bias</style></keyword><keyword><style  face="normal" font="default" size="100%">iBGP policies</style></keyword><keyword><style  face="normal" font="default" size="100%">measurement</style></keyword><keyword><style  face="normal" font="default" size="100%">network topology</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%">June 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;A significant portion of what is known about Internet routing stems out from&amp;nbsp;public BGP datasets. For this reason, numerous research efforts were devoted to&amp;nbsp;(&lt;em&gt;i&lt;/em&gt;) assessing the (in)completeness of the datasets, (&lt;em&gt;ii&lt;/em&gt;) identifying biases&amp;nbsp;in the dataset, and (&lt;em&gt;iii&lt;/em&gt;) augmenting data quality by optimally placing new&amp;nbsp;collectors. However, those studies focused on techniques to extract information&amp;nbsp;about the AS-level Internet topology.&lt;/p&gt;&lt;p&gt;In this paper, we show that considering different metrics influences the&amp;nbsp;conclusions about biases and collector placement. Namely, we compare AS-level&amp;nbsp;topology discovery with \iac inference. We find that the same datasets exhibit&amp;nbsp;significantly diverse biases for these two metrics. For example, the sensitivity&amp;nbsp;to the number and position of collectors is noticeably different. Moreover, for&amp;nbsp;both metrics, the marginal utility of adding a new collector is strongly&amp;nbsp;localized with respect to the proximity of the collector. Our results suggest&amp;nbsp;that the ``optimal'' position for new collectors can only be defined with&amp;nbsp;respect to a specific metric, hence posing a fundamental trade-off for&amp;nbsp;maximizing the utility of extensions to the BGP data collection infrastructure.&lt;/p&gt;</style></abstract></record></records></xml>