Bradley Huffaker

Orbis: Rescaling Degree Correlations to Generate Annotated Internet Topologies

By: 
Priya Mahadevan, Calvin Hubble, Dmitri Krioukov, Bradley Huffaker, and Amin Vahdat
Appears in: 
CCR October 2007

Researchers involved in designing network services and protocols rely on results from simulation and emulation environments to evaluate correctness, performance and scalability. To better understand the behavior of these applications and to predict their performance when deployed across the Internet, the generated topologies that serve as input to simulated and emulated environments must closely match real network characteristics, not just in terms of graph structure (node interconnectivity) but also with respect to various node and link annotations.

AS Relationships: Inference and Validation

By: 
Xenofontas Dimitropoulos, Dmitri Krioukov, Marina Fomenkov, Bradley Huffaker, Young Hyun, kc claffy, and George Riley
Appears in: 
CCR January 2007

Research on performance, robustness, and evolution of the global Internet is fundamentally handicapped without accurate and thorough knowledge of the nature and structure of the contractual relationships between Autonomous Systems (ASs). In this work we introduce novel heuristics for inferring AS relationships. Our heuristics improve upon previous works in several technical aspects, which we outline in detail and demonstrate with several examples. Seeking to increase the value and reliability of our inference results, we then focus on validation of inferred AS relationships.

Public Review By: 
Ernst Biersack

Inferring AS relationships using publicly available data is a difficult task for which various heuristics have been proposed. This paper revisits the problem, points out shortcomings of existing heuristics, and proposes improvements. The reviewers liked the paper for several reasons:

  • The paper does a nice job in reviewing the state of the art and improves on the existing heuristics.
  • The authors try to asses the quality of their inferences by contacting a small group ASs whom they asked for an explicit validation of the results. However, the sample size may be too small to allow any definite conclusions.
  • The heuristics proposed are implemented and the results of the AS inference made publicly available on a weekly basis. This should provide a basis on which further research can build on
    and compare its results against.

In summary, this paper combines existing and new heuristics for AS inference into a tool, the results of which are made available to the community.

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