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Record W2090993616 · doi:10.1109/iscc.2010.5546768

Performance comparison between dynamic protection schemes in Survivable WDM mesh networks

2010· article· en· W2090993616 on OpenAlexaff
Abdelhamid Eshoul, Hussein T. Mouftah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSurvivabilityComputer scienceBlocking (statistics)Adaptive routingComputer networkDistributed computingRouting (electronic design automation)Optical mesh networkWavelength-division multiplexingDynamic Source RoutingComputational complexity theoryRouting protocolTelecommunicationsWireless mesh networkAlgorithmWavelength

Abstract

fetched live from OpenAlex

This paper presents a comparative study between the dynamic survivability approaches in WDM mesh networks. The paper focuses on the diverse routing and the p-cycle approaches to protect mesh networks against single span failure under dynamic traffic. The computational complexity and the blocking performances of both approaches are analyzed and compared. Simulation results suggest that the p-cycle approach has better blocking performance than the diverse routing approach. Additionally, the lower computational complexity of the p-cycle approach algorithm makes it more suitable, especially at highly dynamic traffic. As a result, the p-cycle approach scales better with the network size and the dynamic nature of the traffic than the diverse routing approach. Therefore, the p-cycle approach has presented itself as a better option than the diverse routing to solve the survivability problem in dynamic WDM wavelength-routed networks. Other advantages of the p-cycle approach include their fairness to requests with long routes.

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 score0.471
Threshold uncertainty score0.607

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 integrity0.0000.001
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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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
Published2010
Admission routes1
Has abstractyes

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