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General Identifiability Condition for Network Topology Monitoring with Network Tomography.

Shengli Pan1, Zongwang Zhang2, Zhiyong Zhang3

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Summary
This summary is machine-generated.

Network topology inference using network tomography is challenged by cross-traffic. Accurate relative path length measurements between three paths ensure reliable network topology identification despite traffic variations.

Keywords:
end-to-end measurementnetwork monitoringnetwork tomographytopology identifiability

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Area of Science:

  • Computer Science
  • Network Engineering
  • Data Science

Background:

  • Network topology knowledge is crucial for effective network monitoring and management.
  • Network tomography infers network structure by analyzing end-to-end packet performance.
  • Cross-traffic variability often introduces inconsistencies in path length estimations, biasing topology inference.

Discussion:

  • This research addresses the challenge of accurately identifying network topology in the presence of time-varying cross-traffic.
  • The study proves conditions under which network topology can be reliably determined despite traffic fluctuations.
  • The core insight involves leveraging relative shared path lengths for robust inference.

Key Insights:

  • Accurate network topology identification is achievable even with dynamic cross-traffic.
  • Relative measurements of shared path lengths between any three paths are sufficient for faithful topology recovery.
  • Inconsistencies in absolute path length measurements due to traffic do not preclude accurate topology inference.

Outlook:

  • This work provides a theoretical foundation for robust network tomography.
  • Future research could explore practical implementations and algorithms based on these findings.
  • The results have implications for improving network monitoring and management tools.