Rapier: Integrating routing and scheduling for coflow-aware data center networks
Rapier: Integrating routing and scheduling for coflow-aware data center networks
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DOI：
10.1109/infocom.2015.7218408
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发表时间：
2015-08
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影响因子：
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通讯作者：
Yangming Zhao;Kai Chen;Wei Bai;Minlan Yu;Chen Tian;Yanhui Geng;Yiming Zhang;Dan Li;Sheng Wang-S
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文献类型：
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作者：
Yangming Zhao;Kai Chen;Wei Bai;Minlan Yu;Chen Tian;Yanhui Geng;Yiming Zhang;Dan Li;Sheng Wang-S
在当今数据中心应用程序（例如 MapReduce、Spark 和 Dryad）的数据流模型中，多个流在语义上可以组成一个协同流组。只有完成协同流中的所有流程对应用程序才有意义。为了优化应用程序性能，必须在协同流级别而不是单个流级别上共同考虑路由和调度。然而，现有的解决方案有很大的局限性：它们只考虑调度，这是不够的。为此，我们推出了Rapier，一种协同流感知的网络优化框架，可无缝集成路由和调度，以实现更好的应用程序性能。通过小规模测试台实施和大规模模拟，我们证明与最先进的纯调度解决方案相比，Rapier 显着降低了平均协流完成时间 (CCT) 高达 79.30%，并且很容易使用现有的商品交换机实现。
In the data flow models of today's data center applications such as MapReduce, Spark and Dryad, multiple flows can comprise a coflow group semantically. Only completing all flows in a coflow is meaningful to an application. To optimize application performance, routing and scheduling must be jointly considered at the level of a coflow rather than individual flows. However, prior solutions have significant limitation: they only consider scheduling, which is insufficient. To this end, we present Rapier, a coflow-aware network optimization framework that seamlessly integrates routing and scheduling for better application performance. Using a small-scale testbed implementation and large-scale simulations, we demonstrate that Rapier significantly reduces the average coflow completion time (CCT) by up to 79.30% compared to the state-of-the-art scheduling-only solution, and it is readily implementable with existing commodity switches.