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Machine Learning Versus Threshold Monitoring for Disk Failure Prediction in Data Centers: A Time-Aware Comparison on Public Back blaze Data
Authors
1 2 3 4 5 Department of CSE, APG Shimla University, HP University, Himachal Pradesh, India.
Article Information
- Published In
- Volume 5, Issue 3 (2026)
- Published Online
- September-December 2026
- Pages
- 820-828
- Article Type
- Original Article
References
1. B. Schroeder and G. A. Gibson, “Disk failures in the real world: What does an MTTF of 1,000,000 hours mean to you?,” in Proceedings
of the 5th USENIX Conference on File and Storage Technologies (FAST), pp. 1–16, 2007.
2. E. Pinheiro, W.-D. Weber, and L. A. Barroso, “Failure trends in a large disk drive population,” in Proceedings of the 5th USENIX
Conference on File and Storage Technologies (FAST), pp. 17–28, 2007.
3. B. Schroeder and G. A. Gibson, “A large-scale study of failures in high-performance computing systems,” IEEE Transactions on
Dependable and Secure Computing, vol. 7, no. 4, pp. 337–350, 2010.
4. A. Klein, “What SMART stats tell us about hard drives.” Backblaze Blog, 6 October 2016, https://www.backblaze.com/blog/?p=67631,
2016.
5. G. Hamerly and C. Elkan, “Bayesian approaches to failure prediction for disk drives,” in Proceedings of the 18th Internationa l
Conference on Machine Learning (ICML), pp. 202–209, 2001.
6. G. F. Hughes, J. F. Murray, K. Kreutz-Delgado, and C. Elkan, “Improved disk-drive failure warnings,” IEEE Transactions on
Reliability, vol. 51, no. 3, pp. 350–357, 2002.
7. F. Murray, G. F. Hughes, and K. Kreutz-Delgado, “Machine learning methods for predicting failures in hard drives: A multipleinstance
application,” Journal of Machine Learning Research, vol. 6, pp. 783–816, 2005.
8. S. Lu, B. Luo, T. Patel, Y. Yao, D. Tiwari, and W. Shi, “Making disk failure predictions SMARTer!,” in 18th USENIX Conference on
File and Storage Technologies (FAST 20), pp. 151–167, 2020.
9. R. Pinciroli, L. Yang, J. Alter, and E. Smirni, “Lifespan and failures of SSDs and HDDs: Similarities, differences, and prediction
models,” IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 1, pp. 256–272, 2023.
10. C. Bergmeir and J. M. Benítez, “On the use of cross-validation for time series predictor evaluation,” Information Sciences, vol. 191,
pp. 192–213, 2012.
11. S. Kaufman, S. Rosset, C. Perlich, and O. Stitelman, “Leakage in data mining: Formulation, detection, and avoidance,” ACM
Transactions on Knowledge Discovery from Data, vol. 6, no. 4, 2012.
12. T. Saito and M. Rehmsmeier, “The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on
imbalanced datasets,” PLoS ONE, vol. 10, no. 3, p. e0118432, 2015.
13. H. He and E. A. Garcia, “Learning from imbalanced data,” IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp.
1263–1284, 2009.
14. Backblaze, Inc., “Backblaze drive stats: Hard drive test data.” https://www.backblaze.com/cloud-storage/resources/hard-drive-testdata.
Accessed October 2026.
15. N. Aussel, S. Jaulin, G. Gandon, Y. Petetin, E. Fazli, and S. Chabridon, “Predictive models of hard drive failures based on operational
data,” in Proceedings of the 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 619–625, 2017.
16. Y. Xu, K. Sui, R. Yao, H. Zhang, Q. Lin, Y. Dang, P. Li, K. Jiang, W. Zhang, J.-G. Lou, M. Chintalapati, and D. Zhang, “Improving
service availability of cloud systems by predicting disk error,” in Proceedings of the 2018 USENIX Annual Technical Conference
(USENIX ATC), pp. 481–494, 2018.
17. M. M. Botezatu, I. Giurgiu, J. Bogojeska, and D. Wiesmann, “Predicting disk replacement towards reliable data centers,” in Proceedings
of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 39–48, 2016.
18. F. Mahdisoltani, I. Stefanovici, and B. Schroeder, “Proactive error prediction to improve storage system reliability, ” in Proceedings of
the 2017 USENIX Annual Technical Conference (USENIX ATC), pp. 391–402, 2017.
19. F. Salfner, M. Lenk, and M. Malek, “A survey of online failure prediction methods,” ACM Computing Surveys, vol. 42, no. 3, 2010.
20. P. Notaro, J. Cardoso, and M. Gerndt, “A survey of AIOps methods for failure management,” ACM Transactions on Intelligent Systems
and Technology, vol. 12, no. 6, 2021.
21. T. P. Carvalho, F. A. A. M. N. Soares, R. Vita, R. d. P. Francisco, J. P. Basto, and S. G. S. Alcalá, “A systematic li terature review of
machine learning methods applied to predictive maintenance,” Computers & Industrial Engineering, vol. 137, p. 106024, 2019.
22. G. A. Susto, A. Schirru, S. Pampuri, S. McLoone, and A. Beghi, “Machine learning for predictive maintenance: A mu ltiple classifier
approach,” IEEE Transactions on Industrial Informatics, vol. 11, no. 3, pp. 812–820, 2015.
23. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J.
Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal
of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
24. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
25. J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” The Annals of Statistics, vol. 29, no. 5, pp. 1189 –
1232, 2001.
26. A. Altmann, L. Toloşi, O. Sander, and T. Lengauer, “Permutation importance: A corrected feature importance measure,”
Bioinformatics, vol. 26, no. 10, pp. 1340–1347, 2010.
27. C. Strobl, A.-L. Boulesteix, A. Zeileis, and T. Hothorn, “Bias in random forest variable importance measures: Illustrations, sources and
a solution,” BMC Bioinformatics, vol. 8, p. 25, 2007.
of the 5th USENIX Conference on File and Storage Technologies (FAST), pp. 1–16, 2007.
2. E. Pinheiro, W.-D. Weber, and L. A. Barroso, “Failure trends in a large disk drive population,” in Proceedings of the 5th USENIX
Conference on File and Storage Technologies (FAST), pp. 17–28, 2007.
3. B. Schroeder and G. A. Gibson, “A large-scale study of failures in high-performance computing systems,” IEEE Transactions on
Dependable and Secure Computing, vol. 7, no. 4, pp. 337–350, 2010.
4. A. Klein, “What SMART stats tell us about hard drives.” Backblaze Blog, 6 October 2016, https://www.backblaze.com/blog/?p=67631,
2016.
5. G. Hamerly and C. Elkan, “Bayesian approaches to failure prediction for disk drives,” in Proceedings of the 18th Internationa l
Conference on Machine Learning (ICML), pp. 202–209, 2001.
6. G. F. Hughes, J. F. Murray, K. Kreutz-Delgado, and C. Elkan, “Improved disk-drive failure warnings,” IEEE Transactions on
Reliability, vol. 51, no. 3, pp. 350–357, 2002.
7. F. Murray, G. F. Hughes, and K. Kreutz-Delgado, “Machine learning methods for predicting failures in hard drives: A multipleinstance
application,” Journal of Machine Learning Research, vol. 6, pp. 783–816, 2005.
8. S. Lu, B. Luo, T. Patel, Y. Yao, D. Tiwari, and W. Shi, “Making disk failure predictions SMARTer!,” in 18th USENIX Conference on
File and Storage Technologies (FAST 20), pp. 151–167, 2020.
9. R. Pinciroli, L. Yang, J. Alter, and E. Smirni, “Lifespan and failures of SSDs and HDDs: Similarities, differences, and prediction
models,” IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 1, pp. 256–272, 2023.
10. C. Bergmeir and J. M. Benítez, “On the use of cross-validation for time series predictor evaluation,” Information Sciences, vol. 191,
pp. 192–213, 2012.
11. S. Kaufman, S. Rosset, C. Perlich, and O. Stitelman, “Leakage in data mining: Formulation, detection, and avoidance,” ACM
Transactions on Knowledge Discovery from Data, vol. 6, no. 4, 2012.
12. T. Saito and M. Rehmsmeier, “The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on
imbalanced datasets,” PLoS ONE, vol. 10, no. 3, p. e0118432, 2015.
13. H. He and E. A. Garcia, “Learning from imbalanced data,” IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp.
1263–1284, 2009.
14. Backblaze, Inc., “Backblaze drive stats: Hard drive test data.” https://www.backblaze.com/cloud-storage/resources/hard-drive-testdata.
Accessed October 2026.
15. N. Aussel, S. Jaulin, G. Gandon, Y. Petetin, E. Fazli, and S. Chabridon, “Predictive models of hard drive failures based on operational
data,” in Proceedings of the 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 619–625, 2017.
16. Y. Xu, K. Sui, R. Yao, H. Zhang, Q. Lin, Y. Dang, P. Li, K. Jiang, W. Zhang, J.-G. Lou, M. Chintalapati, and D. Zhang, “Improving
service availability of cloud systems by predicting disk error,” in Proceedings of the 2018 USENIX Annual Technical Conference
(USENIX ATC), pp. 481–494, 2018.
17. M. M. Botezatu, I. Giurgiu, J. Bogojeska, and D. Wiesmann, “Predicting disk replacement towards reliable data centers,” in Proceedings
of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 39–48, 2016.
18. F. Mahdisoltani, I. Stefanovici, and B. Schroeder, “Proactive error prediction to improve storage system reliability, ” in Proceedings of
the 2017 USENIX Annual Technical Conference (USENIX ATC), pp. 391–402, 2017.
19. F. Salfner, M. Lenk, and M. Malek, “A survey of online failure prediction methods,” ACM Computing Surveys, vol. 42, no. 3, 2010.
20. P. Notaro, J. Cardoso, and M. Gerndt, “A survey of AIOps methods for failure management,” ACM Transactions on Intelligent Systems
and Technology, vol. 12, no. 6, 2021.
21. T. P. Carvalho, F. A. A. M. N. Soares, R. Vita, R. d. P. Francisco, J. P. Basto, and S. G. S. Alcalá, “A systematic li terature review of
machine learning methods applied to predictive maintenance,” Computers & Industrial Engineering, vol. 137, p. 106024, 2019.
22. G. A. Susto, A. Schirru, S. Pampuri, S. McLoone, and A. Beghi, “Machine learning for predictive maintenance: A mu ltiple classifier
approach,” IEEE Transactions on Industrial Informatics, vol. 11, no. 3, pp. 812–820, 2015.
23. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J.
Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal
of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
24. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
25. J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” The Annals of Statistics, vol. 29, no. 5, pp. 1189 –
1232, 2001.
26. A. Altmann, L. Toloşi, O. Sander, and T. Lengauer, “Permutation importance: A corrected feature importance measure,”
Bioinformatics, vol. 26, no. 10, pp. 1340–1347, 2010.
27. C. Strobl, A.-L. Boulesteix, A. Zeileis, and T. Hothorn, “Bias in random forest variable importance measures: Illustrations, sources and
a solution,” BMC Bioinformatics, vol. 8, p. 25, 2007.
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