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Record W2052678402 · doi:10.1109/infocom.2014.6847953

Cooperative repair with minimum-storage regenerating codes for distributed storage

2014· article· en· W2052678402 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Storage Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceDistributed data storeConstruct (python library)Bandwidth (computing)Maintenance engineeringComputer data storageDistributed computingComputer networkReliability engineeringComputer hardwareEngineering

Abstract

fetched live from OpenAlex

Distributed storage systems store redundant data to tolerate failures of storage nodes and lost data should be repaired when storage nodes fail. A class of MDS codes, called minimum-storage regenerating (MSR) codes, has been designed to optimize bandwidth consumption when repairing one single failure. Compared with repairing failures individually, the cooperative repair of multiple failures can help to further save bandwidth consumption when multiple failures are being repaired. In this paper, we present a new construction of minimum-storage cooperative regenerating (MSCR) codes that repair two failures cooperatively and exactly. We show that given a valid instance of linear exact MSR codes, we are able to construct a corresponding repair procedure to repair any two failures cooperatively with optimal bandwidth consumption, i.e., to construct an instance of exact MSCR codes directly from exact MSR codes. With this connection, we are also able to repair any single failure exactly with MSCR codes.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.944
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.016
GPT teacher head0.251
Teacher spread0.235 · 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

Quick stats

Citations61
Published2014
Admission routes1
Has abstractyes

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