research-article

Open access

Machine Learning Systems are Bloated and Vulnerable

Authors:

Huaifeng Zhang,

Mohannad Alhanahnah,

Fahmi Abdulqadir Ahmed,

Philipp Leitner,

Ahmed Ali-EldinAuthors Info & Claims

Proceedings of the ACM on Measurement and Analysis of Computing Systems, Volume 8, Issue 1

Article No.: 6, Pages 1 - 30

https://doi.org/10.1145/3639032

Published: 21 February 2024 Publication History

Abstract

Today's software is bloated with both code and features that are not used by most users. This bloat is prevalent across the entire software stack, from operating systems and applications to containers. Containers are lightweight virtualization technologies used to package code and dependencies, providing portable, reproducible and isolated environments. For their ease of use, data scientists often utilize machine learning containers to simplify their workflow. However, this convenience comes at a cost: containers are often bloated with unnecessary code and dependencies, resulting in very large sizes. In this paper, we analyze and quantify bloat in machine learning containers. We develop MMLB, a framework for analyzing bloat in software systems, focusing on machine learning containers. MMLB measures the amount of bloat at both the container and package levels, quantifying the sources of bloat. In addition, MMLB integrates with vulnerability analysis tools and performs package dependency analysis to evaluate the impact of bloat on container vulnerabilities. Through experimentation with 15 machine learning containers from TensorFlow, PyTorch, and Nvidia, we show that bloat accounts for up to 80% of machine learning container sizes, increasing container provisioning times by up to 370% and exacerbating vulnerabilities by up to 99%.

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Cited By

Alhanahnah MBoshmaf YGehani ACraven RMickelson M(2024)SoK: Software Debloating Landscape and Future DirectionsProceedings of the 2024 Workshop on Forming an Ecosystem Around Software Transformation10.1145/3689937.3695792(11-18)Online publication date: 14-Oct-2024
https://dl.acm.org/doi/10.1145/3689937.3695792
Zhang HAlhanahnah MAhmed FFatih DLeitner PAli-Eldin A(2024)Machine Learning Systems are Bloated and VulnerableACM SIGMETRICS Performance Evaluation Review10.1145/3673660.365506452:1(37-38)Online publication date: 13-Jun-2024
https://doi.org/10.1145/3673660.3655064
Zhang HAlhanahnah MAhmed FFatih DLeitner PAli-Eldin AGaretto MMarin ACiucu FFanti GRighter R(2024)Machine Learning Systems are Bloated and VulnerableAbstracts of the 2024 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems10.1145/3652963.3655064(37-38)Online publication date: 10-Jun-2024
https://doi.org/10.1145/3652963.3655064

Index Terms

Machine Learning Systems are Bloated and Vulnerable
1. Software and its engineering
  1. Software notations and tools
    1. Software libraries and repositories

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cover image Proceedings of the ACM on Measurement and Analysis of Computing Systems

Proceedings of the ACM on Measurement and Analysis of Computing Systems Volume 8, Issue 1

POMACS

March 2024

494 pages

EISSN:2476-1249

DOI:10.1145/3649331

Editors:
Augustin Chaintreau
Columbia University
,
Leana Golubchik
University of Southern California, United States
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Zhi-Li Zhang
University of Minnesota, United States

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Copyright © 2024 ACM.

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Published: 21 February 2024

Published in POMACS Volume 8, Issue 1

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Cited By

Alhanahnah MBoshmaf YGehani ACraven RMickelson M(2024)SoK: Software Debloating Landscape and Future DirectionsProceedings of the 2024 Workshop on Forming an Ecosystem Around Software Transformation10.1145/3689937.3695792(11-18)Online publication date: 14-Oct-2024
https://dl.acm.org/doi/10.1145/3689937.3695792
Zhang HAlhanahnah MAhmed FFatih DLeitner PAli-Eldin A(2024)Machine Learning Systems are Bloated and VulnerableACM SIGMETRICS Performance Evaluation Review10.1145/3673660.365506452:1(37-38)Online publication date: 13-Jun-2024
https://doi.org/10.1145/3673660.3655064
Zhang HAlhanahnah MAhmed FFatih DLeitner PAli-Eldin AGaretto MMarin ACiucu FFanti GRighter R(2024)Machine Learning Systems are Bloated and VulnerableAbstracts of the 2024 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems10.1145/3652963.3655064(37-38)Online publication date: 10-Jun-2024
https://doi.org/10.1145/3652963.3655064

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