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Flent: The FLExible Network Tester
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (DISCO)ORCID iD: 0000-0001-5241-6815
University of Modena and Reggio Emilia.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (DISCO)
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (DISCO)
2017 (English)In: Proceedings of 11th EAI International Conference on Performance Evaluation Methodologies and Tools, ACM Digital Library, 2017Conference paper, Published paper (Refereed)
Abstract [en]

Running network performance experiments on real systems is essential for a complete understanding of protocols and systems connected to the internet. However, the process of running experiments can be tedious and error-prone. In particular, ensuring reproducibility across different systems is difficult, and comparing different test runs from an experiment can be non-trivial.

In this paper, we present a tool, called Flent, designed to make experimental evaluations of networks more reliable and easier to perform. Flent works by composing well-known benchmarking tools to, e.g., run tests consisting of several bulk data flows combined with simultaneous latency measurements. Tests are specified in source code, and several common tests are included with the tool. In addition, Flent contains features to automate test runs, collect relevant metadata and interactively plot and explore datasets.

We showcase Flent's capabilities by performing a set of experiments evaluating the new BBR congestion control algorithm, using Flent's capabilities to reproduce experiments both in a controlled testbed and across the public internet. Our evaluation reveals several interesting features of BBR's performance.

Place, publisher, year, edition, pages
ACM Digital Library, 2017.
Keyword [en]
Network experimentation, Network performance analysis, Network measurement, Measurement tools
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kau:diva-64766DOI: 10.1145/3150928.3150957ISBN: 978-1-4503-6346-4 (electronic)OAI: oai:DiVA.org:kau-64766DiVA: diva2:1151589
Conference
VALUETOOLS 2017
Available from: 2017-10-23 Created: 2017-10-23 Last updated: 2018-01-13

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Høiland-Jørgensen, TokeHurtig, PerBrunstrom, Anna
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Total: 140 hits
CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf