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Poster: Empirically Testing the PacketLab Model

Published:24 October 2023Publication History

ABSTRACT

PacketLab is a recently proposed model for accessing remote vantage points. The core design is for the vantage points to export low-level network operations that measurement researchers could rely on to construct more complex measurements. Motivating the model is the assumption that such an approach can overcome persistent challenges such as the operational cost and security concerns of vantage point sharing that researchers face in launching distributed active Internet measurement experiments. However, the limitations imposed by the core design merit a deeper analysis of the applicability of such model to real-world measurements of interest. We undertook this analysis based on a survey of recent Internet measurement studies, followed by an empirical comparison of PacketLab-based versus native implementations of common measurement methods. We showed that for several canonical measurement types common in past studies, PacketLab yielded similar results to native versions of the same measurements. Our results suggest that PacketLab could help reproduce or extend around 16.4% (28 out of 171) of all surveyed studies and accommodate a variety of measurements from latency, throughput, network path, to non-timing data.

References

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  2. Kirill Levchenko, Amogh Dhamdhere, Bradley Huffaker, kc claffy, Mark Allman, and Vern Paxson. 2017. Packetlab: A Universal Measurement Endpoint Interface. In Proceedings of the 2017 Internet Measurement Conference (IMC '17). Association for Computing Machinery, New York, NY, USA, 254--260. https://doi.org/10.1145/3131365.3131396Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Michael Norman, Vince Kellen, Shava Smallen, Brian DeMeulle, Shawn Strande, Ed Lazowska, Naomi Alterman, Rob Fatland, Sarah Stone, Amanda Tan, Katherine Yelick, Eric Van Dusen, and James Mitchell. 2021. CloudBank: Managed Services to Simplify Cloud Access for Computer Science Research and Education. In Practice and Experience in Advanced Research Computing (PEARC '21). Association for Computing Machinery, New York, NY, USA, Article 45, 4 pages. https://doi.org/10.1145/3437359.3465586 ioGoogle ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Conferences
      IMC '23: Proceedings of the 2023 ACM on Internet Measurement Conference
      October 2023
      746 pages
      ISBN:9798400703829
      DOI:10.1145/3618257

      Copyright © 2023 Owner/Author

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 24 October 2023

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      Overall Acceptance Rate277of1,083submissions,26%

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      ACM Internet Measurement Conference
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