On the Quality of BGP Route Collectors for iBGP Policy Inference

A significant portion of what is known about Internet routing stems out from public BGP datasets. For this reason, numerous research efforts were devoted to (i) assessing the (in)completeness of the datasets, (ii) identifying biases
in the dataset, and (iii) augmenting data quality by optimally placing new collectors. However, those studies focused on techniques to extract information about the AS-level Internet topology.

In this paper, we show that considering different metrics influences the conclusions about biases and collector placement. Namely, we compare AS-level topology discovery with \iac inference. We find that the same datasets exhibit significantly diverse biases for these two metrics. For example, the sensitivity to the number and position of collectors is noticeably different. Moreover, for both metrics, the marginal utility of adding a new collector is strongly
localized with respect to the proximity of the collector. Our results suggest that the ``optimal'' position for new collectors can only be defined with respect to a specific metric, hence posing a fundamental trade-off for maximizing the utility of extensions to the BGP data collection infrastructure.