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Record W2144800936 · doi:10.1109/iccs.2006.301466

A User-Controlled Lightpath Provisioning System for Grid Optical Networks

2006· article· en· W2144800936 on OpenAlexafffund
Jing Wu, Michel Savoie, Hanxi Zhang, Scott Campbell

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicPeer-to-Peer Network Technologies
Canadian institutionsCommunications Research Centre Canada
FundersCanarie
KeywordsComputer scienceComputer networkProvisioningDistributed computingScalabilityGridQuality of serviceAutomatically switched optical networkGrid computingBandwidth (computing)Network managementDatabase

Abstract

fetched live from OpenAlex

Grid applications require a network to transport data between geographically distributed sites. The key characteristics of grid networks include adaptability, scalability, heterogeneity, the ability to span different administrative domains, and the support for various QoS levels. Our design objective is to support continuous large traffic flows between a set of known remote sites. We provision dedicated optical connections directly between end user premises. A solution is provided for end users to compose their virtual private networks by using network resources from different suppliers. Our network control mechanism is based on granting end users access to the management interfaces of network elements. End users can reconfigure the route and bandwidth of connections without carrier interventions. In this paper, we propose layered network management architecture for Grid optical networks. An overview of the design of a user-controlled lightpath provisioning system is presented

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.214
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2006
Admission routes2
Has abstractyes

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