Near-Deterministic Inference of AS Relationships

The discovery of Autonomous Systems (ASes) interconnections and the inference of their commercial Type of Relationships (ToR) has been motivated by the need to accurately calculate AS-level paths. An inherent problem in current algorithms is their extensive use of heuristics, causing unbounded errors that are spread over all inferred relationships. We propose a near-deterministic algorithm for solving the ToR inference problem that uses the Internet’s core, a dense sub-graph of top-level ASes.

We test several methods for creating such a core and demonstrate the robustness of the algorithm to the core’s size and density, the inference period, and errors in the core. We evaluate the algorithm using AS-level paths collected from RouteViews BGP paths and DIMES traceroute measurements. Our proposed algorithm deterministically infers over 95% of the approximately 58,000 AS topology links using a week worth of data and as little as 20 ASes in the core. The algorithm infers 2–3 times more peer-to-peer relationships in links discovered only by DIMES than in RouteViews, validating the need for a broad and diverse Internet measurement effort. (pdf) Best Student Paper Award

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