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On survivable traffic grooming over logical topologies in WDM mesh networks

2008· article· en· W2056673472 on OpenAlexafffund
Arunita Jaekel, Ying Chen, Ataul Bari, Subir Bandyopadhyay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceNetwork topologyTraffic groomingScheme (mathematics)Computer networkRouting (electronic design automation)Wavelength-division multiplexingDistributed computingFault (geology)Routing and wavelength assignmentLogical topologyThroughputTopology (electrical circuits)Mesh networkingInteger (computer science)Linear programmingMathematicsAlgorithmTelecommunications

Abstract

fetched live from OpenAlex

Component failure in a WDM network is a serious problem that has attracted considerable attention in recent times. In a standard protection (or restoration) scheme the objective is to preserve the logical topology by switching over to back-up paths (or by setting up new lightpaths) after a fault occurs. In this paper we have proposed a new scheme where we handle a fault simply by modifying the traffic routing scheme to avoid the fault. We show that it is possible to guarantee that a significantly high number of requests for communication can be handled using this scheme, irrespective of the location of the fault. Two new integer linear program formulations have been presented using this approach. The first formulation assumes a fixed RWA, while the second finds the optimal RWA for maximizing guaranteed throughput. A large number simulation experiments demonstrate that, in the vast majority of cases, an optimal solution obtained using our second formulation not only generates a survivable routing but handles all the requests that the original fault-free logical topology was designed to handle.

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 score0.062
Threshold uncertainty score0.688

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 head0.017
GPT teacher head0.225
Teacher spread0.207 · 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
Published2008
Admission routes2
Has abstractyes

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