基于机器学习的分布式互联网路径性能分析

2018 Network Traffic Measurement and Analysis Conference (TMA) Pub Date : 2018-06-01 DOI:10.23919/TMA.2018.8506572

Sarah Wassermann, P. Casas

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引用次数: 0

摘要

互联网路径变化经常与路径膨胀和性能下降有关;因此，预测其发生对性能监控和动态交通工程具有重要意义。在本文中，我们展示了Dis-NETPerf和NETPerfTrace，这两种不同的互补工具，用于使用机器学习模型进行分布式互联网路径性能分析。

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Distributed Internet Paths Performance Analysis Through Machine Learning

Internet path changes are frequently linked to path inflation and performance degradation; therefore, predicting their occurrence is highly relevant for performance monitoring and dynamic traffic engineering. In this paper we showcase Dis-NETPerf and NETPerfTrace, two different and complementary tools for distributed Internet paths performance analysis, using machine learning models.

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来源期刊

2018 Network Traffic Measurement and Analysis Conference (TMA)

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