Algebraic modeling of write amplification in hotness-aware SSD
Why this work is in the frame
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Bibliographic record
Abstract
Garbage collection is essential to the endurance of NAND flash-based solid state drives (SSDs). Analytic modeling of garbage collection reveals the non-trivial relationship between endurance, a performance metric manifested as write amplification, and the algorithmic design variables, device configurations and workload characteristics. This work builds on recent advances in analytic modeling that targets hotness-aware victim selection algorithms, and improves them using a simpler system of algebraic equations. We show that such a model retains the precision of predicting write amplification, with less than 5% error, while reducing the running time from several hours, as the case in previous model of ordinary differential equations, to merely a few seconds. Equipped with such models, we show how design insights can be obtained to guide performance tuning.
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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 | 0.001 |
| 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