Inferring Unseen Components of the Internet Core.

Despite many efforts over the past decade, the ability to generate topological maps of the Internet at the router-level accurately and in a timely fashion remains elusive. Mapping campaigns commonly involve {t traceroute}-like probing that are usually non-adaptive and incomplete, thus revealing only a portion of the underlying topology. In this paper we demonstrate that standard probing methods yield datasets that implicitly contain information about much more than just the directly observed links and routers. Each probe yields information that places constraints on the underlying topology, and by integrating a large number of such constraints it is possible to accurately infer the existence of unseen components of the Internet (i.e., links and routers not directly revealed by the probing). Moreover, we show that this information can be used to adaptively re-focus the probing in order to more quickly discover the topology. These findings suggest radically new and more efficient approaches to Internet mapping. Our work focuses on the discovery of the core of the Internet. We define "Internet core" as the set of routers that is roughly bounded by ingress/egress routers from stub autonomous systems. We describe a novel data analysis methodology designed to accurately infer (i) the number of unseen core routers, (ii) the unseen hop-count distances between observed routers, and (iii) unseen links between observed routers. We use a large experimental dataset to validate the proposed methods. For our data set, we show that our methods can predict the number of unseen routers to within a 13% error level, estimate 60% of the unseen distances between observed routers to within a one-hop error, and robustly detect over 35% of the unseen links between observed routers. Furthermore, we use the information extracted by our inference methodology to drive an adaptive active-probing scheme. The adaptive probing method allows us to generate maps on our data set using 50% fewer probes than standard non-adaptive approaches.