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Dynamic Lightpath Allocation in Survivable Multifiber WDM Netwoks

2006· article· en· W2096752602 on OpenAlexaff
Arunita Jaekel, Yuzhe Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsWavelength-division multiplexingComputer scienceHeuristicMultiplexingComputer networkInteger programmingDistributed computingWavelengthTelecommunicationsAlgorithmOpticsPhysics

Abstract

fetched live from OpenAlex

The deployment of multifiber, wavelength division multiplexing (WDM) networks can offer significant economic benefits over single fiber networks, because of their ability to relax the restrictions due to the wavelength continuity constraint and the potential to handle future growth. In this paper we introduce a new integer linear program (ILP) formulation for dynamic wavelength allocation in survivable, multifiber WDM networks. Single fiber networks, both with and without wavelength conversion, can be treated as a special case of our formulation. We have tested our formulations on several well- known WDM networks and compared the results to single fiber networks. We have also proposed a simple heuristic for dynamic lightpath allocation. Experimental results demonstrate our ILP formulation is feasible for current networks under low to medium traffic. For very large or highly congested networks, the heuristic can be used.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.427

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 head0.004
GPT teacher head0.196
Teacher spread0.192 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

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