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Record W2125615653 · doi:10.1109/icccn.2001.956220

Issues on diverse routing for WDM mesh networks with survivability

2002· article· en· W2125615653 on OpenAlexaff
Pin‐Han Ho, Hussein T. Mouftah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsQueen's University
Fundersnot available
KeywordsSurvivabilityComputer sciencePath (computing)Disjoint setsComputer networkRouting (electronic design automation)Distributed computingWeightingNode (physics)The InternetHeuristicPath protectionSolverWavelength-division multiplexingMathematicsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The task of finding a physically disjoint protection path from a working path to guarantee service continuity during the occurrence of failures has been one of the most important issues for improving survivability of the optical Internet. We focus on the study of finding asymmetrically weighted optimal node-disjoint path-pairs for shared protection so that network performance in terms of success rate of building up disjoint path-pairs and average restoration time are improved. We first propose and examine a novel heuristic algorithm to solve the diverse routing problem in a network with heterogeneous link states for working and protection path pairs, which is based on the sub-optimal path-solver (SOPS), a new method for finding loop-less K-shortest paths. A simulation-based study on the weighting parameter of working paths versus network performance is conducted.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.228
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations27
Published2002
Admission routes1
Has abstractyes

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