Reliable Machine Learning for Networking

Hammerschmidt, C.A.; Garcia, Sebastian; Verwer, S.E.; State, Radu

Reliable Machine Learning for Networking

Key Issues and Approaches

Conference paper (2017)

Authors

C.A. Hammerschmidt University of Luxembourg

Sebastian Garcia Czech Technical University

S.E. Verwer

Radu State University of Luxembourg

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Published Date

2017

Language

English

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Abstract

Machine learning has become one of the go-to methods for solving problems in the field of networking. This development is driven by data availability in large-scale networks and the commodification of machine learning frameworks. While this makes it easier for researchers to implement and deploy machine learning solutions on networks quickly, there are a number of vital factors to account for when using machine learning as an approach to a problem in networking and translate testing performance to real networks deployments successfully. This paper, rather than presenting a particular technical result, discusses the necessary considerations to obtain good results when using machine learning to analyze network-related data.

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