Machine Learning Systems are Bloated and Vulnerable Article Swipe
Huaifeng Zhang
,
Mohannad Alhanahnah
,
Fahmi Abdulqadir Ahmed
,
Dyako Fatih
,
Philipp Leitner
,
Ahmed Ali-Eldin
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3639032
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3639032
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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- en
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- OpenAlex ID
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All OpenAlex metadata
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Machine Learning Systems are Bloated and VulnerableWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-02-16Full publication date if available
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Huaifeng Zhang, Mohannad Alhanahnah, Fahmi Abdulqadir Ahmed, Dyako Fatih, Philipp Leitner, Ahmed Ali-EldinList of authors in order
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https://doi.org/10.1145/3639032Publisher landing page
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https://dl.acm.org/doi/pdf/10.1145/3639032Direct link to full text PDF
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bronzeOpen access status per OpenAlex
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Computer science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 2, 2024: 2Per-year citation counts (last 5 years)
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| abstract_inverted_index.often | 56, 74 |
| abstract_inverted_index.their | 50, 63 |
| abstract_inverted_index.times | 183 |
| abstract_inverted_index.tools | 139 |
| abstract_inverted_index.across | 20 |
| abstract_inverted_index.amount | 117 |
| abstract_inverted_index.bloat. | 131 |
| abstract_inverted_index.entire | 22 |
| abstract_inverted_index.impact | 148 |
| abstract_inverted_index.paper, | 88 |
| abstract_inverted_index.sizes, | 179 |
| abstract_inverted_index.sizes. | 85 |
| abstract_inverted_index.stack, | 24 |
| abstract_inverted_index.users. | 15 |
| abstract_inverted_index.Nvidia, | 165 |
| abstract_inverted_index.Through | 154 |
| abstract_inverted_index.Today's | 0 |
| abstract_inverted_index.analyze | 90 |
| abstract_inverted_index.bloated | 3, 75 |
| abstract_inverted_index.develop | 99 |
| abstract_inverted_index.levels, | 126 |
| abstract_inverted_index.machine | 58, 95, 111, 158, 176 |
| abstract_inverted_index.package | 39, 125, 142 |
| abstract_inverted_index.sources | 129 |
| abstract_inverted_index.systems | 27 |
| abstract_inverted_index.utilize | 57 |
| abstract_inverted_index.However, | 65 |
| abstract_inverted_index.PyTorch, | 163 |
| abstract_inverted_index.accounts | 170 |
| abstract_inverted_index.analysis | 138, 144 |
| abstract_inverted_index.evaluate | 146 |
| abstract_inverted_index.features | 8 |
| abstract_inverted_index.focusing | 109 |
| abstract_inverted_index.isolated | 47 |
| abstract_inverted_index.learning | 59, 96, 112, 159, 177 |
| abstract_inverted_index.measures | 115 |
| abstract_inverted_index.performs | 141 |
| abstract_inverted_index.quantify | 92 |
| abstract_inverted_index.simplify | 62 |
| abstract_inverted_index.software | 1, 23, 107 |
| abstract_inverted_index.systems, | 108 |
| abstract_inverted_index.addition, | 133 |
| abstract_inverted_index.analyzing | 104 |
| abstract_inverted_index.container | 123, 152, 178, 181 |
| abstract_inverted_index.framework | 102 |
| abstract_inverted_index.operating | 26 |
| abstract_inverted_index.portable, | 44 |
| abstract_inverted_index.prevalent | 19 |
| abstract_inverted_index.providing | 43 |
| abstract_inverted_index.resulting | 81 |
| abstract_inverted_index.workflow. | 64 |
| abstract_inverted_index.Containers | 32 |
| abstract_inverted_index.containers | 60, 72, 160 |
| abstract_inverted_index.dependency | 143 |
| abstract_inverted_index.increasing | 180 |
| abstract_inverted_index.integrates | 135 |
| abstract_inverted_index.scientists | 55 |
| abstract_inverted_index.TensorFlow, | 162 |
| abstract_inverted_index.containers. | 31, 97, 113 |
| abstract_inverted_index.convenience | 67 |
| abstract_inverted_index.lightweight | 34 |
| abstract_inverted_index.quantifying | 127 |
| abstract_inverted_index.unnecessary | 77 |
| abstract_inverted_index.applications | 29 |
| abstract_inverted_index.exacerbating | 189 |
| abstract_inverted_index.provisioning | 182 |
| abstract_inverted_index.reproducible | 45 |
| abstract_inverted_index.technologies | 36 |
| abstract_inverted_index.dependencies, | 42, 80 |
| abstract_inverted_index.environments. | 48 |
| abstract_inverted_index.vulnerability | 137 |
| abstract_inverted_index.virtualization | 35 |
| abstract_inverted_index.experimentation | 155 |
| abstract_inverted_index.vulnerabilities | 190 |
| abstract_inverted_index.vulnerabilities. | 153 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.85143746 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |