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Record W1990399092 · doi:10.1109/icton.2012.6253874

Energy-efficient lightpaths for computational grids

2012· article· en· W1990399092 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
KeywordsComputer scienceAnycastNode (physics)Energy consumptionComputer networkDistributed computingBackbone networkEfficient energy useEnergy (signal processing)Sleep modeRouting (electronic design automation)Wavelength-division multiplexingPower (physics)Power consumptionEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Optical networks have been pointed out as strong candidates to ensure energy-efficiency in the Internet backbone, as well as the distributed applications such as computational grids. In this paper, we propose a distributed framework to address energy-efficient lightpath establishment problem for computational grids over the optical WDM backbone. Each backbone node maintains two thresholds to determine its sleep/wakeup cycle. A node in the sleep mode saves energy by rejecting transient traffic. Each demand is routed based on anycast routing and with the objective of minimum power consumption. Through numerical results, we show that significant energy savings are attained by the proposed framework without requiring centralized information and control. We further show that coordinated sleep and wakeup management in the optical backbone can address the trade-off between propagation delay and energy savings.

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: none
Teacher disagreement score0.753
Threshold uncertainty score0.236

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 head0.009
GPT teacher head0.220
Teacher spread0.210 · 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

Citations7
Published2012
Admission routes1
Has abstractyes

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