As modern network applications ( e.g. , large data analytics) become more distributed and can conduct application-layer traffic adaptation, they demand better network visibility to better orchestrate their data flows. As a result, the ability to predict the available bandwidth for a set of flows has become a fundamental requirement of today’s networking systems. While there are previous studies addressing the case of non-reactive flows, the prediction for reactive flows , e.g. , flows managed by TCP congestion control algorithms, still remains an open problem. In this paper, we take the first step to solving this problem in a data center network. To address both theoretical and practical challenges, we introduce a novel learning-based prediction system based on the NUM model, with two key techniques named fast factor learning (FFL) and efficient flow sampling . We adopt novel techniques to overcome practical concerns such as scalability, convergence and unknown system parameters. A system, Prophet, is proposed leveraging the emerging technologies of Software Defined Networking (SDN) to realize the model. Evaluations demonstrate that our solution achieves significant accuracy in a wide range of settings.

中文翻译：

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先知：对DC网络中的无功流进行快速，基于容错的基于模型的吞吐量预测

作为现代网络应用（ 例如 （大数据分析）变得更加分散，并且可以进行应用程序层流量自适应，他们需要更好的网络可见性以更好地协调其数据流。结果，预测一组流的可用带宽的能力已成为当今网络系统的基本要求。尽管以前有研究针对非反应流的情况，但对于反应流 ， 例如 TCP拥塞控制算法管理的流量仍然是一个未解决的问题。在本文中，我们迈出了解决数据中心网络中这一问题的第一步。为了解决理论和实践上的挑战，我们介绍了一种基于NUM模型的新颖的基于学习的预测系统，其中有两项关键技术快速因素学习 （FFL）和 高效流量采样 。我们采用新颖的技术来克服实际问题，例如可伸缩性，收敛性和未知的系统参数。提议利用先知的新兴技术软件定义的网络（SDN）实现模型。评估表明，我们的解决方案在各种设置下均具有很高的精度。