Prophet: Toward Fast, Error-Tolerant Model-Based Throughput Prediction for Reactive Flows in DC Networks
Prophet: Toward Fast, Error-Tolerant Model-Based Throughput Prediction for Reactive Flows in DC Networks
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Prophet：针对直流网络中无功流进行基于快速、容错模型的吞吐量预测
DOI：
10.1109/tnet.2020.3016838
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发表时间：
2020-08
期刊：
影响因子：
--
通讯作者：
Jun Bi
中科院分区：
文献类型：
--
作者：
Jingxuan Zhang;Kai Gao;Y. Richard Yang;Jun Bi
随着现代网络应用程序（例如，大数据分析）变得更加分布并可以进行应用程序层的流量适应，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
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.
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影响因子：
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作者：
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影响因子：
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DOI：
10.1109/icccn.2009.5235248
发表时间：
2009-08
期刊：
2009 Proceedings of 18th International Conference on Computer Communications and Networks
影响因子：
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DOI：
10.1007/978-0-8176-8216-3
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期刊：
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