Virtual Insanity: Linear Subnet Discovery

Over the past two decades, the research community has developed many approaches to study the Internet topology. In particular, starting from 2007, various tools explored the inference of subnets, i.e., sets of devices located on the same connection medium which can communicate directly with each other at the link layer.

In this paper, we first discuss how today's traffic engineering policies increase the difficulty of subnet inference. We carefully characterize typical difficulties and quantify them in the wild. Next, we introduce WISE (Wide and lInear Subnet inferencE), a new tool which tackles those difficulties and discovers, in a linear time, large networks subnets. Based on two ground truth networks, we demonstrate that WISE outperforms state-of-the-art tools. Then, through large-scale
measurements, we show that the selection of a vantage point with WISE has a marginal effect regarding accuracy. Finally, we discuss how subnets can be used to infer neighborhoods (i.e., aggregates of subnets located at most one
hop from each other). We discuss how these neighborhoods can lead to bipartite models of the Internet and present validation results and an evaluation of neighborhoods in the wild, using WISE. Both our code and data are freely
available.