Distributed Internet Paths Performance Analysis through Machine Learning

Abstract : 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 DisNETPerf and NETPerfTrace, two different and complementary tools for distributed Internet paths performance analysis, using machine learning models.
Type de document :
Communication dans un congrès
Demonstrations of the Network Traffic Measurement and Analysis Conference (TMA) 2018, Jun 2018, Vienne, Austria. 〈http://tma.ifip.org/2018/〉
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https://hal.inria.fr/hal-01883815
Contributeur : Sarah Wassermann <>
Soumis le : vendredi 28 septembre 2018 - 17:31:46
Dernière modification le : vendredi 5 octobre 2018 - 16:23:20

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disnetperf_demo_tma18.pdf
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  • HAL Id : hal-01883815, version 1

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Sarah Wassermann, Pedro Casas. Distributed Internet Paths Performance Analysis through Machine Learning. Demonstrations of the Network Traffic Measurement and Analysis Conference (TMA) 2018, Jun 2018, Vienne, Austria. 〈http://tma.ifip.org/2018/〉. 〈hal-01883815〉

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