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Record W2143894190 · doi:10.1109/icc.2002.997333

Path selection with tunnel allocation in the optical Internet based on generalized MPLS architecture

2003· article· en· W2143894190 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 institutionsQueen's University
Fundersnot available
KeywordsMultiprotocol Label SwitchingComputer networkComputer scienceGranularityBandwidth allocationWavelength-division multiplexingLabel switchingBandwidth (computing)Distributed computingWavelengthQuality of serviceMaterials scienceOptoelectronics

Abstract

fetched live from OpenAlex

GMPLS was devised to be able to support multi-granularity traffic and bundling of wavelength channels in the optical domain. It has been a challenge to achieve an efficient and flexible use of the multi-granularity OXCs (MG-OXCs) in the optical next generation Internet which is assumed to deploy a generalized MPLS (GMPLS) based control plane. In this paper, a heuristic algorithm, capacity-balanced static tunnel allocation (CB-STA), is proposed for solving the problem of routing and wavelength assignment with tunneling (RWAT), which is aimed at facilitating an efficient use of bandwidth in the WDM networks with MG-OXCs. CB-STA allocates fiber and waveband tunnels into networks at the network planning stage, which requires each tunnel to have a fixed length and capacity-balanced characteristics, in order to increase the link utilization in the fiber and waveband switching layers. A comparison is made, using simulation, between CB-STA and a dynamic tunnel allocation scheme. Detailed discussions are provided.

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: Empirical · Consensus signal: none
Teacher disagreement score0.700
Threshold uncertainty score0.292

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 head0.008
GPT teacher head0.205
Teacher spread0.197 · 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

Citations14
Published2003
Admission routes1
Has abstractyes

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