Reliable writeback for client-side flash caches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modern data centers are increasingly using shared storage solutions for ease of \nmanagement. Data is cached on the client side on inexpensive and high-capacity \nflash devices, helping improve performance and reduce contention on the storage \nside. Currently, write-through caching is used because it ensures consistency \nand durability under client failures, but it offers poor performance for \nwrite-heavy workloads. \n \nIn this work, we propose two write-back based caching policies, called \nwrite-back flush and write-back persist, that provide strong reliability \nguarantees, under two different client failure models. These policies rely on \nstorage applications such as file systems and databases issuing write barriers \nto persist their data, because these barriers are the only reliable method for \nstoring data durably on storage media. Our evaluation shows that these policies \nachieve performance close to write-back caching, while providing stronger \nguarantees than vanilla write-though caching.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it