Distributed Internet Paths Performance Analysis through Machine Learning

Abstract

International audience; 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.

Original document

The different versions of the original document can be found in:

• https://hal.inria.fr/hal-01883815/file/disnetperf_demo_tma18.pdf

• http://xplorestaging.ieee.org/ielx7/8488332/8506485/08506572.pdf?arnumber=8506572,

http://dx.doi.org/10.23919/tma.2018.8506572

• https://hal.inria.fr/hal-01883815,

https://dblp.uni-trier.de/db/conf/tma/tma2018.html#WassermannC18,

https://hal.inria.fr/hal-01883815/document,

https://academic.microsoft.com/#/detail/2893879868

• https://hal.inria.fr/hal-01883815,

https://hal.inria.fr/hal-01883815/document,

https://hal.inria.fr/hal-01883815/file/disnetperf_demo_tma18.pdf