The Reliability of Modern File Systems in the face of SSD Errors
Bibliographic record
Abstract
As solid state drives (SSDs) are increasingly replacing hard disk drives, the reliability of storage systems depends on the failure modes of SSDs and the ability of the file system layered on top to handle these failure modes. While the classical paper on IRON File Systems provides a thorough study of the failure policies of three file systems common at the time, we argue that 13 years later it is time to revisit file system reliability with SSDs and their reliability characteristics in mind, based on modern file systems that incorporate journaling, copy-on-write, and log-structured approaches and are optimized for flash. This article presents a detailed study, spanning ext4, Btrfs, and F2FS, and covering a number of different SSD error modes. We develop our own fault injection framework and explore over 1,000 error cases. Our results indicate that 16% of these cases result in a file system that cannot be mounted or even repaired by its system checker. We also identify the key file system metadata structures that can cause such failures, and, finally, we recommend some design guidelines for file systems that are deployed on top of SSDs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".