Elephant: the file system that never forgets
Bibliographic record
Abstract
Modern file systems associate the deletion of a file with the release of the storage associated with that file, and file writes with the irrevocable change of file contents. We propose that this model of file system behavior is a relic of the past, when disk storage was a scarce resource. We believe that the correct model should ensure that all user actions are revocable. Deleting a file should change only the name space and file writes should overwrite no old data. The file system, not the user should control storage allocation using a combination of user specified policies and information gleaned from file-edit histories to determine which old versions of a file to retain and for how long. The paper presents the Elephant file system, which provides users with a new contract: Elephant will automatically retain all important versions of the users' files. Users name previous file versions by combining a traditional pathname with a time when the desired version of a file or directory existed. Elephant manages storage at the granularity of a file or groups of files using user-specified retention policies. This approach contrasts with checkpointing file systems such as Plan-9 AFS, and WAFL, that periodically generate efficient checkpoints of entire file systems and thus restrict retention to be guided by a single policy for all files within that file system. We also report on the Elephant prototype, which is implemented as a new Virtual File System in the FreeBSD kernel.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".