Recently, many scheduling schemes leverage coflows to improve the communication performance of jobs in distributed application frameworks deployed in data center networks, such as MapReduce and Spark. Most of them require application modification to obtain the coflow information such as the coflow ID. The latest work CODA suggests non-intrusively extracting coflow information via an identification method. However, the method depends on the historical traffic information, which may cause the identification accuracy to decrease a lot when traffic varies. To tackle the problem, we present SOCI for Scheduling coflows by the Online Coflow Identification. By observing that flows in a coflow typically communicate with a master process for starting and ending in the up-to-date distributed application frameworks, SOCI uses this characteristic for the online coflow identification. Given identification errors are inevitable, the coflow scheduler in SOCI adopts a Selectively Late Binding (SLB) mechanism, which associates the misclassified flows with coflows according to the estimation on the impact of this association on the average Coflow Completion Time (CCT). The trace-driven simulations show that SOCI can reduce CCT by up to $1.23\times$ compared to CODA when the identification accuracy decreases and is comparable to schemes without coflow identification.

中文翻译：

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数据中心网络中通过在线识别来调度Coflow

最近，许多调度方案利用协流来提高数据中心网络中部署的分布式应用框架（例如 MapReduce 和 Spark）中作业的通信性能。其中大多数需要修改应用程序才能获取协流信息，例如协流 ID。最新的工作 CODA 建议通过识别方法非侵入式地提取协流信息。但该方法依赖于历史流量信息，当流量发生变化时，可能会导致识别精度大幅下降。为了解决这个问题，我们提出了通过在线协流识别来调度协流的 SOCI。通过观察协流中的流通常与主进程通信以在最新的分布式应用程序框架中启动和结束，SOCI 使用此特征进行在线协流识别。鉴于识别错误是不可避免的，SOCI中的协流调度器采用选择性延迟绑定（SLB）机制，根据对这种关联对平均协流完成时间（CCT）的影响的估计，将错误分类的流与协流关联起来。迹线驱动的仿真表明 SOCI 可以将 CCT 降低多达$1.23\次$与CODA相比，当识别精度下降时，与没有协流识别的方案相当。