Bibliographic record
Abstract
Disk caches are employed in distributed file systems to avoid network accesses at clients and to compensate for the speed differential between main memory and disk at file servers. Because of concerns about volatility, however, write requests have typically not benefitted from the presence of caches. Instead, they have been processed with some sort of write-through or periodic write-back approach to ensure the integrity of the stored data. The introduction of reasonably priced non-volatile (NV) memories has prompted interest in the use of such memory for write caching, at the server and/or at the client. This paper describes an investigation through trace-driven simulation experiments of several approaches to write caching in distributed systems, with both volatile and non-volatile caches. The results support the findings of earlier work that suggests important differences between caching in the traditional single-level caching environment and caching in a two-level caching environment. While policies focusing on temporal locality perform well for a single-level caching system, or at the client of a two-level caching system, they may not be suitable for use at the server in a two-level caching system. This is because locality characteristics in the reference stream seen at the server in a two-level caching system may be destroyed by caching at the client with a NV write cache large enough to hold the client's working set of dirty blocks. Policies focusing on amortizing the cost of a disk seek operation over multiple write-back operations perform better at the server of a two-level caching system.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| 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".