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Record W2281384273 · doi:10.1109/dsdis.2015.25

A Genetic Algorithm Based Approach for Energy Minimization of Scheduled Traffic in Optical Networks

2015· article· en· W2281384273 on OpenAlexafffund
Arvind Kodakanchi, Ying Chen, Arunita Jaekel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceFitness functionGenetic algorithmEnergy consumptionAnycastRouting (electronic design automation)Sleep modeMinificationSet (abstract data type)Computer networkEnergy (signal processing)Mathematical optimizationPower (physics)Distributed computingPower consumptionEngineeringMathematics

Abstract

fetched live from OpenAlex

In the recent years many researchers have addressed the problem of energy efficient routing in the WDM optical networks. Typical strategies involve putting the network components into sleep mode or switching off the network components in low utilization periods. In this paper, we propose a strategy based on the principle of anycast routing, where it is possible to select the most suitable destination, from a set of available destination nodes to complete the work. A novel genetic algorithm is used for solving this problem for a set of scheduled traffic demands, where the start and end times of the demands are known in advance. The fitness function used in the genetic algorithm not only minimizes the power consumption of the network but also minimizes the overall cost of optical transceivers needed to implement the network.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.405
Threshold uncertainty score0.487

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 head0.015
GPT teacher head0.214
Teacher spread0.199 · 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
GenreMethods

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

Citations0
Published2015
Admission routes2
Has abstractyes

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