Improved Algorithms for Network Topology Discovery

  • Benoit Donnet
  • Timur Friedman
  • Mark Crovella
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3431)


Topology discovery systems are starting to be introduced in the form of easily and widely deployed software. However, little consideration has been given as to how to perform large-scale topology discovery efficiently and in a network-friendly manner. In prior work, we have described how large numbers of traceroute monitors can coordinate their efforts to map the network while reducing their impact on routers and end-systems. The key is for them to share information regarding the paths they have explored. However, such sharing introduces considerable communication overhead. Here, we show how to improve the communication scaling properties through the use of Bloom filters to encode a probing stop set. Also, any system in which every monitor traces routes towards every destination has inherent scaling problems. We propose capping the number of monitors per destination, and dividing the monitors into clusters, each cluster focusing on a different destination list.


False Positive Rate Hash Function Bloom Filter Link Coverage Straight Horizontal Line 
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 2005

Authors and Affiliations

  • Benoit Donnet
    • 1
  • Timur Friedman
    • 1
  • Mark Crovella
    • 2
  1. 1.Laboratoire LiP6-CNRSUniversité Pierre & Marie Curie 
  2. 2.Department of Computer ScienceBoston University 

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