NDLI: bdrmap: Inference of Borders Between IP Networks

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bdrmap: Inference of Borders Between IP Networks

Content Provider	ACM Digital Library
Author	claffy, kc Clark, David Luckie, Matthew Dhamdhere, Amogh Huffaker, Bradley
Abstract	We tackle the tedious and unsolved problem of automatically and correctly inferring network boundaries in traceroute. We explain why such a conceptually simple task is so hard in the real world, and how lack of progress has impeded a wide range of research and development efforts for decades. We develop and validate a method that uses targeted traceroutes, knowledge of traceroute idiosyncrasies, and codification of topological constraints in a structured set of heuristics, to correctly identify interdomain links at the granularity of individual border routers. In this study we focus on the network boundaries we have most confidence we can accurately infer in the presence of sampling bias: interdomain links attached to the network launching the traceroute. We develop a scalable implementation of our algorithm and validate it against ground truth information provided by four networks on 3,277 links, which showed 96.3% -- 98.9% of our inferences were correct. With 19 vantage points (VPs) distributed across a large U.S. broadband provider, we use our method to reveal the tremendous density of router-level interconnection between some ASes. In January 2016, the broadband provider had 45 router-level links with a Tier-1 peer. We also quantify the VP deployment required to observe this ISP's interdomain connectivity, with 17 VPs required to observe all 45 links. Our method forms the cornerstone of the system we are building to map interdomain performance, and we release our code.
Starting Page	381
Ending Page	396
Page Count	16
File Format	PDF
ISBN	9781450345262
DOI	10.1145/2987443.2987467
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-11-14
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Router ownership Internet topology
Content Type	Text
Resource Type	Article

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