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      Inferring persistent interdomain congestion

      Dhamdhere, Amogh; Clark, David D.; Gamero-Garrido, Alexander; Luckie, Matthew John; Mok, Ricky K. P.; Akiwate, Gautam; Gogia, Kabir; Bajpai, Vaibhav; Snoeren, Alex C.; claffy, kc
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      inferring_persistent_interdomain_congestion.pdf
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      DOI
       10.1145/3230543.3230549
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      Dhamdhere, A., Clark, D. D., Gamero-Garrido, A., Luckie, M., Mok, R. K. P., Akiwate, G., … claffy, kc. (2018). Inferring persistent interdomain congestion. In Proceedings of 2018 Conference of the ACM Special Interest Group on Data Communication (SIGCOMM’18) (pp. 1–15). New York, NY, USA: ACM. https://doi.org/10.1145/3230543.3230549
      Permanent Research Commons link: https://hdl.handle.net/10289/13163
      Abstract
      There is significant interest in the technical and policy communities regarding the extent, scope, and consumer harm of persistent interdomain congestion. We provide empirical grounding for discussions of interdomain congestion by developing a system and method to measure congestion on thousands of interdomain links without direct access to them. We implement a system based on the Time Series Latency Probes (TSLP) technique that identifies links with evidence of recurring congestion suggestive of an under-provisioned link. We deploy our system at 86 vantage points worldwide and show that congestion inferred using our lightweight TSLP method correlates with other metrics of interconnection performance impairment. We use our method to study interdomain links of eight large U.S. broadband access providers from March 2016 to December 2017, and validate our inferences against ground-truth traffic statistics from two of the providers. For the period of time over which we gathered measurements, we did not find evidence of widespread endemic congestion on interdomain links between access ISPs and directly connected transit and content providers, although some such links exhibited recurring congestion patterns. We describe limitations, open challenges, and a path toward the use of this method for large-scale third-party monitoring of the Internet interconnection ecosystem.
      Date
      2018
      Type
      Conference Contribution
      Publisher
      ACM
      Rights
      © 2018 ACM. This is the author's accepted version.
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      • Computing and Mathematical Sciences Papers [1455]
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