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Record W197830699

Fault-tolerant grid resource management infrastructure

2004· article· en· W197830699 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

VenueDeakin Research Online (Deakin University) · 2004
Typearticle
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsCarleton University
Fundersnot available
KeywordsGrid computingComputer scienceDistributed computingScalabilityGridSemantic gridDRMAAFault toleranceMiddleware (distributed applications)Utility computingResource management (computing)Shared resourceComputer networkCloud computingOperating systemWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

The main motivation for existing Grid systems is to provide mechanisms for sharing and accessing large and heterogeneous collections of remote resources. This remains the primary goal even today. However, achieving large-scale distributed computing in a seamless manner on Grid computing introduces not only the problem of efficient utilization and satisfactory response time but also the problem of fault-tolerance. With the momentum gaining for the Grid computing, the ability to tolerate failures while effectively exploiting the Grid computing resources in a scalable and transparent manner must be an integral part of Grid computing infrastructure. In this paper, we present a reconfigurable multi-layered Grid infrastructure that provides fault-tolerance mechanisms to ensure that a Grid client can obtain reliable services, even if the middleware service that provides the desired services may suffer from crash failures. © Dynamic Publishers, Inc.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0040.002
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 head0.032
GPT teacher head0.292
Teacher spread0.260 · 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