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Record W2151651568 · doi:10.1109/aina.2007.120

Relaxed Failure Recovery Process for Reliable Quality-of-Service using Unreserved Backup Paths

2007· article· en· W2151651568 on OpenAlexaff
I‐Chen Wu, M.R. Ito

Bibliographic record

VenueProceedings · 2007
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBackupComputer sciencePath (computing)Quality of serviceComputer networkService qualityDistributed computingService (business)Database

Abstract

fetched live from OpenAlex

Guaranteed, high-quality service in the computer network often requires a single reserved path. However, a single path can easily suffer from single point failures. This paper provides a solution to handle flows when their reserved primary paths fail. The solution pairs a reserved primary path with an unreserved backup path. When a failure occurs and a primary path fails, the initial service guarantee is relaxed slightly, and the flow with the failed primary path is re-routed on the least congested alternate path. This approach allows all flows to continue despite a failure in the network. In addition, even though the service quality is relaxed to accommodate flows with failed primary paths, by carefully planning the least congested alternate paths as backups, very high levels of service quality can still be retained after failure.

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.

How this classification was reachedexpand

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.052
GPT teacher head0.311
Teacher spread0.259 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2007
Admission routes1
Has abstractyes

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