Analytical modeling of garbage collection algorithms in hotness-aware flash-based solid state drives
Why this work is in the frame
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Bibliographic record
Abstract
Garbage collection plays a central role of flash-based solid state drive performance, in particular, its endurance. Analytical modeling is an indispensable instrument for design improvement as it demonstrates the relationship between SSD endurance, manifested as write amplification, and the algorithmic design variables, as well as workload characteristics. In this paper, we improve recent advances in using the mean field analysis as a tool for performance analysis and target hotness-aware flash management algorithms. We show that even under a generic workload model, the system dynamics can be captured by a system of ordinary differential equations, and the steady-state write amplification can be predicted for a variety of practical garbage collection algorithms, including the d-Choice algorithm. Furthermore, the analytical model is validated by a large collection of real and synthetic traces, and prediction errors against these simulations are shown to be within 5%.
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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 | 0.001 |
| 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