Robust Data Preprocessing for Machine-Learning-Based Disk Failure Prediction in Cloud Production Environments Article Swipe
Shujie Han
,
Jun Wu
,
Erci Xu
,
Cheng He
,
Patrick P. C. Lee
,
Yi Qiang
,
Qixing Zheng
,
Tao Huang
,
Zixi Huang
,
Rui Li
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1912.09722
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1912.09722
To provide proactive fault tolerance for modern cloud data centers, extensive studies have proposed machine learning (ML) approaches to predict imminent disk failures for early remedy and evaluated their approaches directly on public datasets (e.g., Backblaze SMART logs). However, in real-world production environments, the data quality is imperfect (e.g., inaccurate labeling, missing data samples, and complex failure types), thereby degrading the prediction accuracy. We present RODMAN, a robust data preprocessing pipeline that refines data samples before feeding them into ML models. We start with a large-scale trace-driven study of over three million disks from Alibaba Cloud's data centers, and motivate the practical challenges in ML-based disk failure prediction. We then design RODMAN with three data preprocessing echniques, namely failure-type filtering, spline-based data filling, and automated pre-failure backtracking, that are applicable for general ML models. Evaluation on both the Alibaba and Backblaze datasets shows that RODMAN improves the prediction accuracy compared to without data preprocessing under various settings.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1912.09722
- https://arxiv.org/pdf/1912.09722
- OA Status
- green
- Cited By
- 11
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2996388742
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2996388742Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1912.09722Digital Object Identifier
- Title
-
Robust Data Preprocessing for Machine-Learning-Based Disk Failure Prediction in Cloud Production EnvironmentsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-12-20Full publication date if available
- Authors
-
Shujie Han, Jun Wu, Erci Xu, Cheng He, Patrick P. C. Lee, Yi Qiang, Qixing Zheng, Tao Huang, Zixi Huang, Rui LiList of authors in order
- Landing page
-
https://arxiv.org/abs/1912.09722Publisher landing page
- PDF URL
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https://arxiv.org/pdf/1912.09722Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1912.09722Direct OA link when available
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Computer science, Preprocessor, Data mining, Cloud computing, Data pre-processing, Pipeline (software), Missing data, Machine learning, Artificial intelligence, Programming language, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 2, 2023: 2, 2022: 2, 2021: 2Per-year citation counts (last 5 years)
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35Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.machine | 14 |
| abstract_inverted_index.million | 91 |
| abstract_inverted_index.missing | 51 |
| abstract_inverted_index.models. | 80, 133 |
| abstract_inverted_index.predict | 19 |
| abstract_inverted_index.present | 64 |
| abstract_inverted_index.provide | 1 |
| abstract_inverted_index.quality | 45 |
| abstract_inverted_index.refines | 72 |
| abstract_inverted_index.samples | 74 |
| abstract_inverted_index.studies | 11 |
| abstract_inverted_index.thereby | 58 |
| abstract_inverted_index.types), | 57 |
| abstract_inverted_index.various | 155 |
| abstract_inverted_index.without | 151 |
| abstract_inverted_index.However, | 38 |
| abstract_inverted_index.ML-based | 104 |
| abstract_inverted_index.accuracy | 148 |
| abstract_inverted_index.centers, | 9, 97 |
| abstract_inverted_index.compared | 149 |
| abstract_inverted_index.datasets | 33, 141 |
| abstract_inverted_index.directly | 30 |
| abstract_inverted_index.failures | 22 |
| abstract_inverted_index.filling, | 122 |
| abstract_inverted_index.imminent | 20 |
| abstract_inverted_index.improves | 145 |
| abstract_inverted_index.learning | 15 |
| abstract_inverted_index.motivate | 99 |
| abstract_inverted_index.pipeline | 70 |
| abstract_inverted_index.proposed | 13 |
| abstract_inverted_index.samples, | 53 |
| abstract_inverted_index.Backblaze | 35, 140 |
| abstract_inverted_index.accuracy. | 62 |
| abstract_inverted_index.automated | 124 |
| abstract_inverted_index.degrading | 59 |
| abstract_inverted_index.evaluated | 27 |
| abstract_inverted_index.extensive | 10 |
| abstract_inverted_index.imperfect | 47 |
| abstract_inverted_index.labeling, | 50 |
| abstract_inverted_index.practical | 101 |
| abstract_inverted_index.proactive | 2 |
| abstract_inverted_index.settings. | 156 |
| abstract_inverted_index.tolerance | 4 |
| abstract_inverted_index.Evaluation | 134 |
| abstract_inverted_index.applicable | 129 |
| abstract_inverted_index.approaches | 17, 29 |
| abstract_inverted_index.challenges | 102 |
| abstract_inverted_index.echniques, | 116 |
| abstract_inverted_index.filtering, | 119 |
| abstract_inverted_index.inaccurate | 49 |
| abstract_inverted_index.prediction | 61, 147 |
| abstract_inverted_index.production | 41 |
| abstract_inverted_index.real-world | 40 |
| abstract_inverted_index.large-scale | 85 |
| abstract_inverted_index.pre-failure | 125 |
| abstract_inverted_index.prediction. | 107 |
| abstract_inverted_index.failure-type | 118 |
| abstract_inverted_index.spline-based | 120 |
| abstract_inverted_index.trace-driven | 86 |
| abstract_inverted_index.backtracking, | 126 |
| abstract_inverted_index.environments, | 42 |
| abstract_inverted_index.preprocessing | 69, 115, 153 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile |