Checking the Integrity of Transactional Mechanisms
Bibliographic record
Abstract
Data corruption is the most common consequence of file-system bugs. When such corruption occurs, offline check and recovery tools must be used, but they are error prone and cause significant downtime. Previously we showed that a runtime checker for the Ext3 file system can verify that metadata updates are consistent, helping detect corruption in metadata blocks at transaction commit time. However, corruption can still occur when a bug in the file system’s transactional mechanism loses, misdirects, or corrupts writes. We show that a runtime checker must enforce the atomicity and durability properties of the file system on every write, in addition to checking transactions at commit time, to provide the strong guarantee that every block write will maintain file system consistency. We identify the invariants that need to be enforced on journaling and shadow paging file systems to preserve the integrity of committed transactions. We also describe the key properties that make it feasible to check these invariants for a file system. Based on this characterization, we have implemented runtime checkers for Ext3 and Btrfs. Our evaluation shows that both checkers detect data corruption effectively, and they can be used during normal operation with low overhead.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".