Revisiting TCP congestion control throughput models & fairness properties at scale
Revisiting TCP congestion control throughput models & fairness properties at scale
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DOI：
10.1145/3487552.3487834
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Adithya Abraham Philip;Ranysha Ware;Rukshani Athapathu;Justine Sherry;Vyas Sekar
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作者：
Adithya Abraham Philip;Ranysha Ware;Rukshani Athapathu;Justine Sherry;Vyas Sekar
我们对拥塞控制算法（CCA）吞吐量和公平性的大部分理解都来自于（隐含地）假设拥塞发生在最后一英里的模型和测量。也就是说，这些研究在几十个流和高达几百Mbps带宽的规模下评估了“小规模”边缘设置中的CCA。然而，最近的测量表明，拥塞也可能发生在互联网的核心提供商之间的链接，其中成千上万的流共享高带宽的链接。因此，一个自然的问题是：我们对CCA吞吐量和公平性的理解是否继续保持在互联网核心的规模上，具有1000个流量和Gbps带宽？我们的初步实验研究发现，在边缘设置中得出的一些期望并不符合规模。例如，使用丢失率作为马西斯模型的参数来估计TCP NewReno吞吐量在边缘设置中工作良好，但当数千个流在高带宽上竞争时，无法提供准确的吞吐量估计。此外，BBR -在边缘与其他BBR流竞争时实现良好的公平性-在互联网核心的规模上可能对其他BBR流变得非常不公平。在本文中，我们讨论了这些结果和其他人，以及未来的CCA分析和评价的关键影响。
Much of our understanding of congestion control algorithm (CCA) throughput and fairness is derived from models and measurements that (implicitly) assume congestion occurs in the last mile. That is, these studies evaluated CCAs in "small scale" edge settings at the scale of tens of flows and up to a few hundred Mbps bandwidths. However, recent measurements show that congestion can also occur at the core of the Internet on inter-provider links, where thousands of flows share high bandwidth links. Hence, a natural question is: Does our understanding of CCA throughput and fairness continue to hold at the scale found in the core of the Internet, with 1000s of flows and Gbps bandwidths? Our preliminary experimental study finds that some expectations derived in the edge setting do not hold at scale. For example, using loss rate as a parameter to the Mathis model to estimate TCP NewReno throughput works well in edge settings, but does not provide accurate throughput estimates when thousands of flows compete at high bandwidths. In addition, BBR - which achieves good fairness at the edge when competing solely with other BBR flows - can become very unfair to other BBR flows at the scale of the core of the Internet. In this paper, we discuss these results and others, as well as key implications for future CCA analysis and evaluation.