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Record W2159579318 · doi:10.1109/ccece.2003.1226009

Dynamic routing algorithms in all-optical networks

2004· article· en· W2159579318 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
KeywordsComputer scienceNetwork topologyRouting (electronic design automation)HeuristicLink-state routing protocolStatic routingFast protein liquid chromatographyHierarchical routingComputer networkDistributed computingBlocking (statistics)Routing protocolAlgorithmArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we consider dynamic routing in all-optical networks without wavelength converters which are very expensive and always not effective. We propose two new heuristic algorithms to manage optical routing based on link-state and reduce blocking probability of request arriving in the network. The first one uses a technique similar to "fixed paths least congested" (FPLC) routing by analyzing the first k links on each path whereas the second is based on an estimation of the link-congestion in the network. Both algorithms achieve good performance, for different types of network topologies, when compared to existing methods like FPLC, LLR, and FPLC-k.

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: Methods · Consensus signal: none
Teacher disagreement score0.680
Threshold uncertainty score0.558

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 head0.238
Teacher spread0.229 · 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

Citations17
Published2004
Admission routes1
Has abstractyes

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