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Monitoring Latency on Submarine Cables: Limitations and Opportunities

Authors Mia Weaver , Darryl Veitch , Paul Barford , Fabián E. Bustamante , Esteban Carisimo

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Subject Classification

ACM Subject Classification
  • Networks → Network measurement
Keywords
  • Submarine Cable Network
  • Internet Measurement

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Abstract

The world’s Submarine Cable Network (SCN) is a critical component of the Internet, supporting both inter- and intra- continental communication. We describe a methodology to extract packet latency (via RTT) information on submarine segments, using existing deployed infrastructure collecting standard traceroute measurements. The first component of the methodology identifies vantage points whose measurements traverse submarine segments. The second component provides novel, path-change-aware, approaches to extract the minRTT over the segments, using those vantage points. We demonstrate the efficacy of our method by using traceroute measurements from perfSONAR deployments as ground truth. Our results provide a clear view of the inherent limitations of existing deployed infrastructure. Although both our methodology and minimum RTT estimators raise the state of the art, they also reveal that such infrastructure cannot hope, in general, to access dynamic latency metrics such as latency variability (variance) arising from congestion.

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Mia Weaver, Darryl Veitch, Paul Barford, Fabián E. Bustamante, and Esteban Carisimo. Monitoring Latency on Submarine Cables: Limitations and Opportunities. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 16:1-16:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/OASIcs.NINeS.2026.16

Author Details

Mia Weaver
  • University of Wisconsin, Madison, WI, USA
Darryl Veitch
  • University of Technology Sydney, Australia
Paul Barford
  • University of Wisconsin, Madison, WI, USA
Fabián E. Bustamante
  • Northwestern University, Evanston, IL, USA
Esteban Carisimo
  • Northwestern University, Evanston, IL, USA

Acknowledgements

The authors want to thank our shepherd Justine Sherry for her insights and thoughts on improvements that we were able to make in the paper. This work is supported by the National Science Foundation grants CNS-2107392, CNS-1703592, CNS-2039146, and CNS-2106517. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Darryl Veitch is partially supported by the Australian Research Council’s Discovery Projects funding scheme #DP230102856.

Supplementary Materials

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