拥塞控制网络的瓶颈结构研究

Jordi Ros-Giralt, Atul Bohara, Sruthi Yellamraju, Harper Langston, R. Lethin, Yuang Jiang, L. Tassiulas, Josie Li, Yuanlong Tan, M. Veeraraghavan
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引用次数: 1

摘要

本文引入瓶颈排序理论,这是一个揭示数据网络瓶颈结构的数学框架。该理论框架至少在三个方面提供了对网络固有拓扑特性的见解:(1)它确定了每个瓶颈的影响区域;(2)揭示了分布式拥塞控制算法中瓶颈(以及流经它们的流)收敛到稳态传输速率的顺序;(3)为优化交通工程策略的设计提供了重要的见解。我们证明了所提出的理论在TCP拥塞控制网络中对两大类算法的有效性:基于拥塞的算法(TCP BBR)和基于损失的加增/乘减算法(TCP Cubic和Reno)。在其他结果中,我们的网络实验表明:(1)定性地说,两类拥塞控制算法的行为都符合网络瓶颈结构的预测;(2)流只与在同一瓶颈水平运行的其他流竞争带宽;(3)由于BBR流能够在合适的瓶颈水平运行,因此比Cubic和Reno流具有更高的性能和公平性;(4)网络的瓶颈结构是不断变化的,由于流量往返时间的变化,瓶颈的水平可以折叠;(5)与传统观点相反,低命中率流量会对网络的整体性能产生很大影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the Bottleneck Structure of Congestion-Controlled Networks
In this paper, we introduce the Theory of Bottleneck Ordering, a mathematical framework that reveals the bottleneck structure of data networks. This theoretical framework provides insights into the inherent topological properties of a network in at least three areas: (1) It identifies the regions of influence of each bottleneck; (2) it reveals the order in which bottlenecks (and flows traversing them) converge to their steady state transmission rates in distributed congestion control algorithms; and (3) it provides key insights into the design of optimized traffic engineering policies. We demonstrate the efficacy of the proposed theory in TCP congestion-controlled networks for two broad classes of algorithms: Congestion-based algorithms (TCP BBR) and loss-based additive-increase/multiplicative-decrease algorithms (TCP Cubic and Reno). Among other results, our network experiments show that: (1) Qualitatively, both classes of congestion control algorithms behave as predicted by the bottleneck structure of the network; (2) flows compete for bandwidth only with other flows operating at the same bottleneck level; (3) BBR flows achieve higher performance and fairness than Cubic and Reno flows due to their ability to operate at the right bottleneck level; (4) the bottleneck structure of a network is continuously changing and its levels can be folded due to variations in the flows' round trip times; and (5) against conventional wisdom, low-hitter flows can have a large impact to the overall performance of a network.
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