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.
|Title of host publication||2017 IEEE 42nd conference on Local Computer Networks, LCN 2017|
|Number of pages||4|
|Publication status||Published - 2017|
|Event||2017 IEEE 42nd Conference on Local Computer Networks, LCN 2017 - Singapore, Singapore|
Duration: 9 Oct 2017 → 12 Oct 2017
|Conference||2017 IEEE 42nd Conference on Local Computer Networks, LCN 2017|
|Period||9/10/17 → 12/10/17|