Document - Helping your docker images to spread based on explainable models

2019

Conference article Restricted

Helping your docker images to spread based on explainable models

Guidotti R., Soldani J., Neri D., Brogi A., Pedreschi D.

Explainable models Popularity estimation Docker images

Docker 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: https://github.com/di-unipi-socc/DockerImageMiner.

Source: ECML-PKDD 2018, pp. 205–221, Dublin, Ireland, 10/09/2018 - 14/09/2018

Publisher: Springer, Berlin, DEU

Metrics

Back to previous page

Cite as

BibTeX entry

@inproceedings{oai:it.cnr:prodotti:417428,
	title = {Helping your docker images to spread based on explainable models},
	author = {Guidotti R. and Soldani J. and Neri D. and Brogi A. and Pedreschi D.},
	publisher = {Springer, Berlin, DEU},
	doi = {10.1007/978-3-030-10997-4_13},
	booktitle = {ECML-PKDD 2018, pp. 205–221, Dublin, Ireland, 10/09/2018 - 14/09/2018},
	year = {2019}
}