论文标题

数据网络瓶颈结构的定量理论

A Quantitative Theory of Bottleneck Structures for Data Networks

论文作者

Ros-Giralt, Jordi, Amsel, Noah, Yellamraju, Sruthi, Ezick, James, Lethin, Richard, Jiang, Yuang, Feng, Aosong, Tassiulas, Leandros

论文摘要

数据网络中拥塞控制问题的常规视图是基于以下原则:流动的性能是由其瓶颈链接状态(无论网络的拓扑特性如何)所唯一决定的。但是,最近的工作表明,通过解释瓶颈链接之间相互作用的模型,可以更好地解释拥堵控制网络的行为。这些相互作用由潜在\ textit {瓶颈结构}捕获,该模型描述了复杂的连锁效应,该效果会在网络的一个部分在另一部分施加的一部分变化。在本文中,我们提出了一个\ textit {定量}瓶颈结构理论(QTB),这是一个数学和工程框架,其中包括一个多项式时间算法的家族,可用于推理各种网络优化问题,包括路由,容量计划和流程控制。 QTB可以通过清楚地预测替代流程路线的相对性能,并为交通塑料的最佳速率设置提供数值建议,从而为交通工程做出贡献。能力计划领域的一个特别新颖的结果表明,当流量受到拥塞控制时,先前建立的折叠网络设计规则是次优的。我们表明,QTB可用于得出这一重要类别的拓扑类别的最佳规则,并使用BBR和立方充血控制算法在经验上证明了这些结果的正确性和功效。

The conventional view of the congestion control problem in data networks is based on the principle that a flow's performance is uniquely determined by the state of its bottleneck link, regardless of the topological properties of the network. However, recent work has shown that the behavior of congestion-controlled networks is better explained by models that account for the interactions between bottleneck links. These interactions are captured by a latent \textit{bottleneck structure}, a model describing the complex ripple effects that changes in one part of the network exert on the other parts. In this paper, we present a \textit{quantitative} theory of bottleneck structures (QTBS), a mathematical and engineering framework comprising a family of polynomial-time algorithms that can be used to reason about a wide variety of network optimization problems, including routing, capacity planning and flow control. QTBS can contribute to traffic engineering by making clear predictions about the relative performance of alternative flow routes, and by providing numerical recommendations for the optimal rate settings of traffic shapers. A particularly novel result in the domain of capacity planning indicates that previously established rules for the design of folded-Clos networks are suboptimal when flows are congestion controlled. We show that QTBS can be used to derive the optimal rules for this important class of topologies, and empirically demonstrate the correctness and efficacy of these results using the BBR and Cubic congestion-control algorithms.

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