CLIC: client-informed caching for storage servers
Why this work is in the frame
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Bibliographic record
Abstract
Traditional caching policies are known to perform poorly for storage server caches. One promising approach to solving this problem is to use hints from the storage clients to manage the storage server cache. Previous hinting approaches are ad hoc, in that a predefined reaction to specific types of hints is hard-coded into the caching policy. With ad hoc approaches, it is difficult to ensure that the best hints are being used, and it is difficult to accommodate multiple types of hints and multiple client applications. In this paper, we propose CLient-Informed Caching (CLIC), a generic hint-based policy for managing storage server caches. CLIC automatically interprets hints generated by storage clients and translates them into a server caching policy. It does this without explicit knowledge of the application-specific hint semantics. We demonstrate using trace-based simulation of database workloads that CLIC outperforms hintoblivious and state-of-the-art hint-aware caching policies. We also demonstrate that the space required to track and interpret hints is small. 1
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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 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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