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.
Original languageEnglish
Title of host publication2017 IEEE 42nd conference on Local Computer Networks, LCN 2017
Number of pages4
ISBN (Electronic)978-1-5090-6523-3
ISBN (Print)978-1-5090-6524-0
Publication statusPublished - 2017
Event2017 IEEE 42nd Conference on Local Computer Networks, LCN 2017 - Singapore, Singapore
Duration: 9 Oct 201712 Oct 2017


Conference2017 IEEE 42nd Conference on Local Computer Networks, LCN 2017

ID: 33113699