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Record W2133684921 · doi:10.1109/icton.2006.248402

Optimum Planning of GMPLS Transport Networks

2006· article· en· W2133684921 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
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceComputer networkMultiprotocol Label SwitchingScalabilityInteger programmingNode (physics)Routing (electronic design automation)Wavelength-division multiplexingRouting and wavelength assignmentGranularityDistributed computingWavelengthEngineeringAlgorithmQuality of service

Abstract

fetched live from OpenAlex

The rapid increase in the number of wavelengths per fiber has significantly increased the size of the optical crossconnect (OXC) in WDM transport networks. To maintain the scalability of the OXC at a reasonable level, various multi-granular OXC (MG-OXC) architectures have been proposed. Motivated by the fact that the MG-OXC constitutes only the optical segment of the generalized multi-protocol label switching (GMPLS)-based transport node architecture, we first propose a novel node architecture that handles the whole traffic hierarchy defined in GMPLS. Second, incorporating the proposed architecture with our novel contributions, namely (1) bifurcation of multi-granularity traffic demands; (2) traffic flow grouping strategies at all granularity levels, requires defining a new transport planning problem, which we call the routing and multi-granular paths assignment (RMGPA) problem. The RMGPA problem is formulated as a mixed integer linear programming (MILP) model with the objective of minimizing the overall network weighted port count

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.822
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.206
Teacher spread0.199 · 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

Citations19
Published2006
Admission routes1
Has abstractyes

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