Evaluation of a Large-Scale Topology Discovery Algorithm

  • Benoit Donnet
  • Bradley Huffaker
  • Timur Friedman
  • kc claffy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4268)


In the past few years, the network measurement community has been interested in the problem of internet topology discovery using a large number (hundreds or thousands) of measurement monitors. The standard way to obtain information about the internet topology is to use the traceroute tool from a small number of monitors. Recent papers have made the case that increasing the number of monitors will give a more accurate view of the topology. However, scaling up the number of monitors is not a trivial process. Duplication of effort close to the monitors wastes time by reexploring well-known parts of the network, and close to destinations might appear to be a distributed denial-of-service (DDoS) attack as the probes converge from a set of sources towards a given destination. In prior work, authors of this paper proposed Doubletree, an algorithm for cooperative topology discovery, that reduces the load on the network, i.e., router IP interfaces and end-hosts, while discovering almost as many nodes and links as standard approaches based on traceroute. This paper presents our open-source and freely downloadable implementation of Doubletree in a tool we call traceroute@home. We evaluate the performance of our implementation on the PlanetLab testbed and discuss a large-scale monitoring infrastructure that could benefit of Doubletree.


Monitoring Component IPv6 Network Internet Topology Measurement Infrastructure Topology Discovery 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Benoit Donnet
    • 1
    • 2
  • Bradley Huffaker
    • 2
  • Timur Friedman
    • 1
  • kc claffy
    • 2
  1. 1.Laboratoire LiP6/CNRS, UMRUniversité Pierre & Marie CurieFrance
  2. 2.San Diego Supercomputer CenterCaidaUSA

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