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Record W2104634478 · doi:10.1109/icton.2009.5185139

Performance evaluation of dynamic P-cycle protection methods in WDM optical networks

2009· article· en· W2104634478 on OpenAlexaff
Abdelhamid Eshoul, Hussein T. Mouftah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBlocking (statistics)Wavelength-division multiplexingComputer scienceComputer networkOptical mesh networkRouting (electronic design automation)Scheme (mathematics)Mesh networkingConstraint (computer-aided design)Distributed computingAdaptive routingPath protectionRouting and wavelength assignmentWavelengthStatic routingTelecommunicationsEngineeringRouting protocolWireless mesh networkOpticsMathematics

Abstract

fetched live from OpenAlex

The primary objectives in the design of survivable WDM mesh networks are maximizing network efficiency and minimizing restoration time. P-cycle based protection scheme can achieve both objectives simultaneously. However, configuring p-cycles dynamically in WDM mesh networks under wavelength continuity constraint poses a major challenge due to the wavelength blocking that may take place during the setup of a new lightpath. This paper investigates ways of configuring p-cycles statically and dynamically in WDM mesh networks under dynamic traffic and wavelength continuity constraint. Several dynamic configuration options are analyzed and their performances are evaluated. To improve the blocking performance, two routing and wavelength assignment schemes are proposed. The blocking performances of the different configuration methods, together with different dynamic routing strategies, have been evaluated and compared using simulations.

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)0.001
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: none
Teacher disagreement score0.474
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.320
Teacher spread0.300 · 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
Published2009
Admission routes1
Has abstractyes

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