Efficient network tomography for internet topology discovery

Brian Eriksson, Gautam Dasarathy, Paul Barford, Robert Nowak

Research output: Contribution to journalArticle

22 Scopus citations

Abstract

Accurate and timely identification of the router-level topology of the Internet is one of the major unresolved problems in Internet research. Topology recovery via tomographic inference is potentially an attractive complement to standard methods that use TTL-limited probes. Unfortunately, limitations of prior tomographic techniques make timely resolution of large-scale topologies impossible due to the requirement of an infeasible number of measurements. In this paper, we describe new techniques that aim toward efficient tomographic inference for accurate router-level topology measurement. We introduce methodologies based on Depth-First Search (DFS) ordering that clusters end-hosts based on shared infrastructure and enables the logical tree topology of a network to be recovered accurately and efficiently. We evaluate the capabilities of our algorithms in large-scale simulation and find that our methods will reconstruct topologies using less than 2% of the measurements required by exhaustive methods and less than 15% of the measurements needed by the current state-of-the-art tomographic approach. We also present results from a study of the live Internet where we show our DFS-based methodologies can recover the logical router-level topology more accurately and with fewer probes than prior techniques.

Original languageEnglish (US)
Article number6216499
Pages (from-to)931-943
Number of pages13
JournalIEEE/ACM Transactions on Networking
Volume20
Issue number3
DOIs
StatePublished - Jun 1 2012
Externally publishedYes

Keywords

  • Internet topology
  • network monitoring
  • network tomography
  • routing topology inference
  • sampling methods
  • unicast

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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