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Record W2885725796

Geriatrix: aging what you see and what you don't see a file system aging approach for modern storage systems

2018· article· en· W2885725796 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

VenueUSENIX Annual Technical Conference · 2018
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Storage Technologies
Canadian institutionsVector Institute
Fundersnot available
KeywordsFile systemComputer scienceFragmentation (computing)Operating systemSoftwareComputer data storageFlash memoryEmbedded system
DOInot available

Abstract

fetched live from OpenAlex

File system performance on modern primary storage devices (Flash-based SSDs) is greatly affected by aging of the free space, much more so than were mechanical disk drives. We introduce Geriatrix, a simple-to-use profile driven file system aging tool that induces target levels of fragmentation in both allocated files (what you see) and remaining free space (what you don't see), unlike previous approaches that focus on just the former. This paper describes and evaluates the effectiveness of Geriatrix, showing that it recreates both fragmentation effects better than previous approaches. Using Geriatrix, we show that measurements presented in many recent file systems papers are higher than should be expected, by up to 30% on mechanical (HDD) and up to 80% on Flash (SSD) disks. Worse, in some cases, the performance rank ordering of file system designs being compared are different from the published results. Geriatrix will be released as open source software with eight built-in aging profiles, in the hopes that it can address the need created by the increased performance impact of file system aging in modern SSD-based storage.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0030.009
Open science0.0030.002
Research integrity0.0000.001
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 head0.028
GPT teacher head0.267
Teacher spread0.239 · 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