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Record W2120235380 · doi:10.1109/ccece.2002.1015238

Routing and wavelength assignment for permanent and reliable wavelength paths in WDM networks

2003· article· en· W2120235380 on OpenAlexaff
Steven Chamberland, D.O. Khyda, Samuel Pierre

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsRouting and wavelength assignmentRouting (electronic design automation)Computer scienceHeuristicWavelength-division multiplexingStatic routingInteger programmingComputer networkPath (computing)Link-state routing protocolMultipath routingMathematical optimizationWavelengthRouting protocolAlgorithmMathematics

Abstract

fetched live from OpenAlex

We tackle the routing and wavelength assignment problem for wavelength division multiplexing (WDM) networks containing permanent and reliable wavelength paths (WPs). It consists of finding the routes and the wavelength assignment for the normal state of the network and for the important failure scenarios. These scenarios might be the most probable failure scenarios or simply the failure scenarios of interest to the network planner (e.g., the single link failure scenarios). We propose an integer mathematical programming model for this problem. This model supposes that the routing is based on a weighted shortest path policy. From the implementation simplicity and the network performance standpoint, this type of routing is the best one. In order to find solutions, a greedy heuristic is proposed. This heuristic first finds the routing of the permanent and reliable WPs in the normal state of the network and assign heuristically wavelength to WPs. Next, the failure scenarios are treated separately. Finally, a detailed example 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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score0.638

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 head0.009
GPT teacher head0.211
Teacher spread0.202 · 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

Citations3
Published2003
Admission routes1
Has abstractyes

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