Reliable writeback for client-side flash caches
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.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".