SmartHeating: On the Performance and Lifetime Improvement of Self-Healing SSDs
Bibliographic record
Abstract
In NAND flash memory-based solid-state drives (SSDs), during the idle time between the consecutive program/erase cycles (dwell time), the dielectric damage of flash cell can be partially repaired, also known as the self-recovery effect. As the effectiveness of the self-recovery effect can be improved under high temperature, self-healing SSDs are proven feasible to extend the flash endurance significantly. However, current self-healing SSDs perform the heating operations on all the worn-out blocks without considering the data retention requirement, and measures the lifetime of flash memory based on the worst-case self-recovery effect, leading to some unnecessary heating operations and the degraded performance. We propose SmartHeating, a smart heating scheme that exploits the dwell time variation and the write hotness variation to improve the I/O performance and the lifetime of self-healing SSDs. SmartHeating tracks the dwell time of all worn-out flash blocks, predicts their self-recovery effect and reliability, and avoids performing heating operations on the worn-out flash blocks that still have strong flash reliability. In addition, by exploiting the data hotness variation, SmartHeating only heats the worn-out flash blocks that store write-cold data, while allocating write-hot data to a small portion of worn-out flash blocks with negligible refresh overhead. The experimental results show that SmartHeating reduces the number of heating operations by 12.5% on average, boosts I/O performance of flash storage systems by 21.0%, and improves the lifetime of flash memory by $1.20\times $ compared with conventional heating scheme.
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".