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Evaluating and modeling window synchronization in highly multiplexed flows

机译:在高度复用的流中评估和建模窗口同步

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In this paper, we investigate issues of synchronization in highly aggregated flows such as would be found in the Internet backbone. Our hypothesis is that regularly spaced loss events lead to window synchronization in long lived flows. We argue that window synchronization is likely to be more common in the Internet than previously reported. We support our argument with evidence of the existence and evaluation of the characteristics of periodic discrete congestion events using active probe data gathered in the Surveyor infrastructure. When connections experience loss events which are periodic, the aggregate offered load to neighboring links rises and falls in cadence with the loss events. Connections whose cWnd values grow from W/2 to W at approximately the same rate as the loss event period soon synchronize their cWnd additive increases and multiplicative decreases. We find that this window synchronization can scale to large numbers of connections depending on the diversity of roundtrip times of individual flows. A model is presented that predicts important characteristics of the loss events in window synchronized flows including the quantity, intensity, and duration. The model effectively explains the prevalence of discrete loss events in fast links with high multiplexing factors as well as the queue buildup and queue draining phases of congestion.
机译:在本文中,我们研究了高度聚合的流中的同步问题,例如在Internet主干网中会发现的同步问题。我们的假设是,规则间隔的损失事件会导致长寿命流中的窗口同步。我们认为,窗口同步在Internet中可能比以前报道的更为普遍。我们使用存在于Surveyor基础设施中的主动探测数据来证明周期性离散性拥塞事件的特征的存在和评估,以此来支持我们的论点。当连接遇到周期性的丢失事件时,向相邻链路提供的聚合负载随丢失事件的节奏而上升或下降。连接的cWnd值从W / 2增长到W的速率几乎与丢失事件周期相同,因此它们的cWnd加性增加和乘法性减少很快同步。我们发现,根据各个流的往返时间的多样性,此窗口同步可以扩展到大量连接。提出了一个模型,该模型可预测窗口同步流中损失事件的重要特征,包括数量,强度和持续时间。该模型有效地解释了具有高复用因子的快速链路中离散丢失事件的普遍性,以及拥塞的队列建立和队列排空阶段。

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