数据中心 TCP 数据集
Jan Fesl1,2, Tereza Čapková1, Michal Konopa1
1Faculty of Economics, Department of Applied Mathematics and Informatics, University of South Bohemia in České Budějovice, Czechia.
Data in brief
|June 3, 2024
概括
本研究介绍了一个全面的数据集,比较了17个传输控制协议 (TCP) 版本及其拥堵控制算法 (CCA). 这些数据有助于优化TCP性能和开发新的基于机器学习的CCA.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 数据科学数据科学数据科学
背景情况:
- 传输控制协议 (TCP) 对可靠的数据传输至关重要.
- 不同的TCP版本使用不同的拥堵控制算法 (CCA) 来管理网络流量.
- 在数据中心环境中,优化TCP性能至关重要.
研究的目的:
- 引入一个独特的数据集,用于将17个TCP版本与不同CCA进行比较.
- 为评估TCP性能指标提供一个全面的资源.
- 通过机器学习促进新型CCA的开发.
主要方法:
- 在数据中心使用TCP收集了成千上万个网络流量测量.
- 测量每个TCP流50次以确保数据稳定性和可靠性.
- 分析了每个TCP传输的18个定量属性.
主要成果:
- 一个独特的数据集,比较17个TCP版本和他们的CCA现在可用.
- 该数据集包括18个定量属性,用于详细的绩效分析.
- 这些数据可以直接比较和识别性能特征.
结论:
- 该数据集适用于测试和比较现有的TCP版本.
- 它支持创建和验证新的CCA,特别是使用机器学习的CCA.
- 这些发现可以导致当前TCP实现的优化.
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