Many emerging distributed applications, including big data analytics, generate a number of flows that concurrently transport data across data center networks. To improve their performance, it is required to account for the behavior of a collection of flows, i.e., coflows, rather than individual. State-of-the-art solutions allow for a near-optimal completion time by continuously reordering the unfinished coflows at the end-host, using network priorities. This paper shows that dynamically changing flow priorities at the end host, without taking into account in-flight packets, can cause high-degrees of packet re-ordering, thus imposing pressure on the congestion control and potentially harming network performance in the presence of switches with shallow buffers. We present pCoflow, a new solution that integrates end-host based coflow ordering with in-network scheduling based on packet history. Our evaluation shows that pCoflow improves in CCT upon state-of-the-art solutions by up to 34% for varying load.

中文翻译：

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为 Coflow 调度提供网络支持

许多新兴的分布式应用程序（包括大数据分析）会生成大量流，这些流同时跨数据中心网络传输数据。为了提高它们的性能，需要考虑流集合的行为，即协流，而不是单个流。最先进的解决方案通过使用网络优先级在终端主机上连续重新排序未完成的协流，从而获得接近最佳的完成时间。本文表明，在不考虑传输中的数据包的情况下，在终端主机上动态改变流优先级会导致高度的数据包重新排序，从而对拥塞控制施加压力，并可能在存在交换机的情况下损害网络性能带有浅缓冲区。我们介绍 pCoflow，一种新的解决方案，将基于终端主机的 coflow 排序与基于数据包历史的网络内调度相结合。我们的评估表明，在不同负载下，pCoflow 在最先进的解决方案上的 CCT 提高了 34%。