Inferring AS Relationships: Dead End or Lively Beginning?

TL;DR

This paper examines the limitations of current AS relationship inference methods, identifies causes of inaccuracies, and proposes a novel optimization-based approach to improve the accuracy of inferred AS hierarchies.

Contribution

It introduces a multiobjective optimization framework with node-degree corrections and applies semidefinite programming to enhance AS relationship inference accuracy.

Findings

More accurate AS hierarchy inference achieved

Reduced invalid BGP path predictions

Outperforms recent heuristic methods

Abstract

Recent techniques for inferring business relationships between ASs have yielded maps that have extremely few invalid BGP paths in the terminology of Gao. However, some relationships inferred by these newer algorithms are incorrect, leading to the deduction of unrealistic AS hierarchies. We investigate this problem and discover what causes it. Having obtained such insight, we generalize the problem of AS relationship inference as a multiobjective optimization problem with node-degree-based corrections to the original objective function of minimizing the number of invalid paths. We solve the generalized version of the problem using the semidefinite programming relaxation of the MAX2SAT problem. Keeping the number of invalid paths small, we obtain a more veracious solution than that yielded by recent heuristics.