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Explaining Successful Docker Images Using Pattern Mining Analysis

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Software Technologies: Applications and Foundations (STAF 2018)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11176))

Abstract

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 directly impacts on its usage, and hence on the potential revenues of its developers. In this paper, we present a frequent pattern mining-based approach for understanding how to improve an image to increase its popularity. The results in this work can provide valuable insights to Docker image providers, helping them to design more competitive software products.

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Notes

  1. 1.

    An example of raw Docker image data is available at https://goo.gl/hibue1.

  2. 2.

    As images are downloaded as compressed archives, their download size correspond to their compressed size (in GBs).

  3. 3.

    The actual size of an image corresponds to its decompressed size (in GBs).

  4. 4.

    Publicly available at https://goo.gl/ggvKN3.

  5. 5.

    http://www.borgelt.net/pyfim.html.

  6. 6.

    The python code and the list of all the itemsets and popularity rules extracted can be found at https://github.com/di-unipi-socc/DockerImageMiner.

  7. 7.

    We discarded very similar rules in order to have a broader overview.

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Acknowledgments

Work partly supported by the EU H2020 Program under the funding scheme “INFRAIA-1-2014-2015: Research Infrastructures” grant agreement 654024 “SoBigData” http://www.sobigdata.eu.

Author information

Authors and Affiliations

  1. University of Pisa, Largo B. Pontecorvo, 3, Pisa, Italy

    Riccardo Guidotti, Jacopo Soldani, Davide Neri & Antonio Brogi

  2. KDDLab, ISTI-CNR, Via G. Moruzzi, 1, Pisa, Italy

    Riccardo Guidotti

Authors
  1. Riccardo Guidotti
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  2. Jacopo Soldani
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  3. Davide Neri
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  4. Antonio Brogi
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Corresponding author

Correspondence to Riccardo Guidotti .

Editor information

Editors and Affiliations

  1. Innopolis University, Innopolis, Russia

    Manuel Mazzara

  2. University of Toulouse, Toulouse Cedex 9, France

    Iulian Ober

  3. Grenoble Alpes University, Montbonnot, France

    Gwen Salaün

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Guidotti, R., Soldani, J., Neri, D., Brogi, A. (2018). Explaining Successful Docker Images Using Pattern Mining Analysis. In: Mazzara, M., Ober, I., Salaün, G. (eds) Software Technologies: Applications and Foundations. STAF 2018. Lecture Notes in Computer Science(), vol 11176. Springer, Cham. https://doi.org/10.1007/978-3-030-04771-9_9

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  • DOI: https://doi.org/10.1007/978-3-030-04771-9_9

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