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Improving Coverage of Internet Outage Detection in Sparse Blocks

  • Guillermo BaltraEmail author
  • John Heidemann
Conference paper
  • 55 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12048)

Abstract

There is a growing interest in carefully observing the reliability of the Internet’s edge. Outage information can inform our understanding of Internet reliability and planning, and it can help guide operations. Active outage detection methods provide results for more than 3M blocks, and passive methods more than 2M, but both are challenged by sparse blocks where few addresses respond or send traffic. We propose a new Full Block Scanning (FBS) algorithm to improve coverage for active scanning by providing reliable results for sparse blocks by gathering more information before making a decision. FBS identifies sparse blocks and takes additional time before making decisions about their outages, thereby addressing previous concerns about false outages while preserving strict limits on probe rates. We show that FBS can improve coverage by correcting 1.2M blocks that would otherwise be too sparse to correctly report, and potentially adding 1.7M additional blocks. FBS can be applied retroactively to existing datasets to improve prior coverage and accuracy.

Notes

Acknowledgments

We thank Yuri Pradkin for his input on the algorithms and paper.

We thank Philipp Richter and Arthur Berger for discussions about their work, and Philipp for re-running his comparison with CDN data.

The work is supported in part by the National Science Foundation, CISE Directorate, award CNS-1806785; by the Department of Homeland Security (DHS) Science and Technology Directorate, Cyber Security Division (DHS S&T/CSD) via contract number 70RSAT18CB0000014; and by Air Force Research Laboratory under agreement number FA8750-18-2-0280. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon.

Supplementary material

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of Southern CaliforniaLos AngelesUSA
  2. 2.Information Sciences InstituteMarina del ReyUSA

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