IBU: An In-Block Update Address Mapping Scheme for Solid-State Drives
Bibliographic record
Abstract
One of the most important features of solid-state drives (SSDs) is the ability to update data sectors out of place, performing garbage collection operations within its physical blocks, to mitigate limited flash memory lifespan. In SSDs, a firmware called the Flash Translation Layer (FTL) is used to hide such features from the operating system. One way to extend the lifespan of flash memory is to use data compression at the FTL level. In compression-based FTLs, the address mapping type has a significant effect on the efficiency and read/write speed. The type of address mapping in previous similar schemes is based on page-level address translation and requires high memory for the mapping table. In this paper, we propose a compression-based hybrid FTL called In Block Update Address Mapping, in which the pages of each logical block are compressed, and fewer physical pages are written in physical blocks. Therefore, a small number of free pages remain. In the proposed scheme, the required memory of the mapping table has been reduced by 78% when compared to similar schemes. Moreover, extensive simulation results reported in this paper show that the proposed FTL scheme outperforms other FTL schemes in the read and write operations under realistic benchmark workloads.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".