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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.
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Policy challenges in mapping Internet interdomain congestion
Interconnection links connecting broadband access providers with their peers, transit providers and major content providers, are a potential point of discriminatory treatment and impairment of user experience. However, adequate data to shed light on this situation is lacking, and different actors can put forward opportunistic interpretations of data to support their points of view. In this article, we introduce a topology-aware model of interconnection to elucidate our own beliefs about how to measure interconnection links of access providers and how policy- makers should interpret the results. We use six case studies that show how our conceptual model can guide a critical analysis of what is or should be measured and reported, and how to soundly interpret these measurements.
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- Award ID(s):
- 1724853
- PAR ID:
- 10186678
- Date Published:
- Journal Name:
- Journal of information policy
- Volume:
- 10
- ISSN:
- 2158-3897
- Page Range / eLocation ID:
- 1-44
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/https://www.doi.org/10.5325/jinfopoli.10.2020.0001
