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Record W2012922054 · doi:10.1002/nem.696

A network management tool for resource‐partition based layer 1 virtual private networks

2008· article· en· W2012922054 on OpenAlexafffundabout
Jing Wu, Michel Savoie, Scott Campbell, Hanxi Zhang

Bibliographic record

VenueInternational Journal of Network Management · 2008
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsCommunications Research Centre Canada
FundersDalhousie UniversityCanarie
KeywordsComputer sciencePrivate networkPartition (number theory)Computer networkDatabase transactionApplication layerNetwork elementDistributed computingDatabaseOperating system

Abstract

fetched live from OpenAlex

Abstract A Layer 1 Virtual Private Network (L1‐VPN) has two models for service management: the resource‐partition based model and the domain‐service based model. In this paper, we present a network management tool for resource‐partition based L1‐VPNs. A Transaction Language One (TL1) proxy is designed to achieve resource partitioning at the network element level. Building on top of a TL1 proxy, we implemented a User‐Controlled LightPath (UCLP) system to support physical network brokers to assign and allocate virtually dedicated resources to customers, and to enable customers to directly manage their resources. With such a capability, customers are able to create wide area networks based on their traffic pattern, and to adjust their traffic pattern based on available resources. Copyright © 2008 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.234
Teacher spread0.218 · 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

Citations1
Published2008
Admission routes3
Has abstractyes

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