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Record W1988210464 · doi:10.1587/transinf.e93.d.1644

A Buffer Management Issue in Designing SSDs for LFSs

2010· article· en· W1988210464 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEICE Transactions on Information and Systems · 2010
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Storage Technologies
Canadian institutionsKootenay Association for Science & Technology
FundersKorea Evaluation Institute of Industrial Technology
KeywordsComputer scienceWrite bufferBuffer (optical fiber)Key (lock)TRACE (psycholinguistics)CacheSystemCBandwidth (computing)Embedded systemOperating systemComputer networkCPU cacheCache algorithmsTelecommunications

Abstract

fetched live from OpenAlex

This letter introduces a buffer management issue in designing SSDs for log-structured file systems (LFSs). We implemented a novel trace-driven SSD simulator in SystemC language, and simulated several SSD architectures with the NILFS2 trace. From the results, we give two major considerations related to the buffer management as follows. (1) The write buffer is used as a buffer not a cache, since all write requests are sequential in NILFS2. (2) For better performance, the main architectural factor is the bus bandwidth, but 332MHz is enough. Instead, the read buffer makes a key role in performance improvement while caching data. To enhance SSDs, accordingly, it is an effective way to make efficient read buffer management policies, and one of the examples is tracking the valid data zone in NILFS2, which can increase the data hit ratio in read buffers significantly.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.252
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it