Helping your docker images to spread based on explainable models

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TitleHelping your docker images to spread based on explainable models
Publication TypeConference Paper
Year of Publication2018
AuthorsGuidotti, R, Soldani, J, Neri, D, Brogi, A, Pedreschi, D
Conference NameJoint European Conference on Machine Learning and Knowledge Discovery in Databases
AbstractDocker is on the rise in today’s enterprise IT. It permits shipping applications inside portable containers, which run from so-called Docker images. Docker images are distributed in public registries, which also monitor their popularity. The popularity of an image impacts on its actual usage, and hence on the potential revenues for its developers. In this paper, we present a solution based on interpretable decision tree and regression trees for estimating the popularity of a given Docker image, and for understanding how to improve an image to increase its popularity. The results presented in this work can provide valuable insights to Docker developers, helping them in spreading their images. Code related to this paper is available at: