Storage Configuration Drift After Automated Deployment: A Longitudinal Study of Detection Latency and Remediation Workload
Abstract
Configuration drift can undermine the long-term reliability of automated infrastructure even when initial deployment succeeds. This 16-week longitudinal study followed 72 storage environments after automated provisioning to determine how quickly drift emerged, how it was detected, and how much remediation effort it created. Desired-state definitions were compared daily with observed configurations across capacity, snapshot, access-control, replication, and performance-policy settings. Drift events were independently classified by origin and severity. A total of 286 drift events were identified; 61% followed emergency operator changes or maintenance activity, while 23% were associated with configuration dependencies not represented in the original automation. Continuous state comparison detected high-severity drift in a median of 2.4 hours, compared with 39.7 hours for routine operational review. Early detection reduced median remediation effort from 46 to 19 minutes per event and was associated with fewer repeated incidents. Drift affecting replication and access control produced the greatest operational impact. Automated deployment should therefore be treated as the beginning of configuration control rather than its end. Continuous desired-state monitoring and explicit exception handling substantially reduced the persistence and workload associated with post-deployment drift.
References
1. Nazir M. Automating enterprise storage provisioning with PowerShell and Python: effects on deployment time and configuration errors. Journal of Multidisciplinary Research and Integrated Sciences. 2021;1(1). Available from: https://jmris.com/index.php/jmris/article/view/2021P1V6
2. Rahman A, Mahdavi-Hezaveh R, Williams L. A systematic mapping study of infrastructure as code research. Inf Softw Technol. 2019;108:65-77. doi:10.1016/j.infsof.2018.12.004.
3. Sharma T, Fragkoulis M, Spinellis D. Does your configuration code smell?. In: Proceedings of the 13th Working Conference on Mining Software Repositories. 2016. p. 189-200. doi:10.1145/2901739.2901761.
4. Rahman A, Williams L. Source code properties of defective infrastructure as code scripts. Inf Softw Technol. 2019;112:148-163. doi:10.1016/j.infsof.2019.04.013.
5. Hanappi O, Hummer W, Dustdar S. Asserting reliable convergence for configuration management scripts. In: Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications. 2016. p. 328-343. doi:10.1145/2983990.2984000.
6. Rahman A, Elder S, Shezan FH, Frost V, Stallings J, Williams L. Bugs in infrastructure as code. arXiv [Preprint]. 2018. doi:10.48550/arXiv.1809.07937.
7. Rahman A, Farhana E, Williams L. The 'as code' activities: development anti-patterns for infrastructure as code. arXiv [Preprint]. 2020. doi:10.48550/arXiv.2006.00177.
8. Mytkowicz T, Diwan A, Hauswirth M, Sweeney PF. Producing wrong data without doing anything obviously wrong. In: Proceedings of the 14th International Conference on Architectural Support for Programming Languages and Operating Systems. 2009. p. 265-276. doi:10.1145/1508244.1508275.
9. Kalibera T, Jones R. Rigorous benchmarking in reasonable time. In: Proceedings of the 2013 International Symposium on Memory Management. 2013. p. 63-74. doi:10.1145/2464157.2464160.
10. Dean J, Barroso LA. The tail at scale. Commun ACM. 2013;56(2):74-80. doi:10.1145/2408776.2408794.
11. Rose S, Borchert O, Mitchell S, Connelly S. Zero trust architecture. Gaithersburg (MD): National Institute of Standards and Technology. 2020;NIST SP 800-207. doi:10.6028/NIST.SP.800-207.
12. Swanson M, Bowen P, Phillips AW, Gallup D, Lynes D. Contingency planning guide for federal information systems. Gaithersburg (MD): National Institute of Standards and Technology. 2010;NIST SP 800-34 Rev. 1. doi:10.6028/NIST.SP.800-34r1.
13. Lakens D. Sample size justification. Collabra Psychol. 2022;8(1):33267. doi:10.1525/collabra.33267.
14. Nosek BA, Ebersole CR, DeHaven AC, Mellor DT. The preregistration revolution. Proc Natl Acad Sci U S A. 2018;115(11):2600-2606. doi:10.1073/pnas.1708274114.
15. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3:160018. doi:10.1038/sdata.2016.18.
16. Lakens D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol. 2013;4:863. doi:10.3389/fpsyg.2013.00863.
17. Efron B. Bootstrap methods: another look at the jackknife. Ann Stat. 1979;7(1):1-26. doi:10.1214/aos/1176344552.
18. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol. 1995;57(1):289-300. doi:10.1111/j.2517-6161.1995.tb02031.x.
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