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Record W2547323896 · doi:10.1109/honet.2012.6421450

Design considerations for energy-efficient Multi-Granular Optical Networks

2012· article· en· W2547323896 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 institutionsUniversity of Ottawa
Fundersnot available
KeywordsScalabilityComputer scienceBandwidth (computing)Traffic groomingComputer networkEnergy consumptionRouting (electronic design automation)HeuristicThe InternetBackbone networkRouting and wavelength assignmentTransmission (telecommunications)Wavelength-division multiplexingPower consumptionNetwork planning and designDistributed computingPower (physics)TelecommunicationsWavelengthElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Multi-Granular Optical Networks (MG-ONs) offer enhanced bandwidth utilization, reduced transmission cost and increased scalability in the optical Internet backbone. There have been several proposals for optimized design of MG-ONs as power consumption in the Internet backbone has recently become an important concern. Although recent research has shown that multi-granular switching concept can guarantee energy savings when compared to the traditional traffic grooming, MG-ON design still calls for legitimate and self-contained design specifications. In this paper, we revisit the Routing and MultiGranular Path Assignment (RMGPA) problem by having the objective of minimized power consumption. Heuristic solutions of RMGPA problem show that crucial power and cost savings can be achieved with the proper selection of the wavelength capacity, waveband size and number of wavebands per fiber with a certain fiber capacity.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.487
Threshold uncertainty score0.525

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 head0.036
GPT teacher head0.247
Teacher spread0.211 · 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

Citations8
Published2012
Admission routes1
Has abstractyes

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