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Data center TCP dataset.

Jan Fesl1,2, Tereza Čapková1, Michal Konopa1

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Summary
This summary is machine-generated.

This study introduces a comprehensive dataset comparing 17 Transmission Control Protocol (TCP) versions and their congestion control algorithms (CCAs). The data aids in optimizing TCP performance and developing new machine learning-based CCAs.

Keywords:
Congestion controlData centerIdentificationMachine learningTCP

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Area of Science:

  • Computer Science
  • Network Engineering
  • Data Science

Background:

  • Transmission Control Protocol (TCP) is fundamental for reliable data transfer.
  • Various TCP versions employ diverse congestion control algorithms (CCAs) to manage network traffic.
  • Optimizing TCP performance is crucial in data center environments.

Purpose of the Study:

  • To introduce a unique dataset for comparing 17 TCP versions with different CCAs.
  • To provide a comprehensive resource for evaluating TCP performance metrics.
  • To facilitate the development of novel CCAs using machine learning.

Main Methods:

  • Collected thousands of network flow measurements using TCP in a data center.
  • Measured each TCP flow 50 times to ensure data stability and reliability.
  • Analyzed 18 quantitative attributes for each TCP transmission.

Main Results:

  • A unique dataset comparing 17 TCP versions and their CCAs is now available.
  • The dataset includes 18 quantitative attributes for detailed performance analysis.
  • The data enables direct comparison and identification of performance characteristics.

Conclusions:

  • The dataset is suitable for testing and comparing existing TCP versions.
  • It supports the creation and validation of new CCAs, particularly those using machine learning.
  • The findings can lead to the optimization of current TCP implementations.