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Record W2146995992 · doi:10.1109/ccece.2003.1226007

Improvement of routing and wavelength assignment in WDM networks using tabu search

2004· article· en· W2146995992 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 institutionsPolytechnique Montréal
Fundersnot available
KeywordsRouting and wavelength assignmentTabu searchComputer scienceRouting (electronic design automation)Wavelength-division multiplexingComputer networkMetric (unit)Link-state routing protocolMathematical optimizationDistributed computingAlgorithmWavelengthRouting protocolMathematicsEngineering

Abstract

fetched live from OpenAlex

In this paper, we deal with the routing and wavelength assignment (RWA) problem for wavelength division multiplexing (WDM) networks containing permanent and reliable wavelength paths (WPs). This problem consists of finding the routes of the WPs 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). More specifically, we analyze the effect of the value of the routing link metric vector on the solution cost defined has the total number of blocked WPs. We propose an algorithm based on the tabu search principle that tunes the routing link metrics in order to improve the solution cost.

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: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.384

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 head0.013
GPT teacher head0.234
Teacher spread0.220 · 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

Citations6
Published2004
Admission routes1
Has abstractyes

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