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Record W2112762862 · doi:10.1109/glocom.2007.448

Heuristics for Planning GMPLS Networks with Conversion and Regeneration Capabilities

2007· article· en· W2112762862 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
KeywordsHeuristicsComputer scienceScalabilityInteger programmingMultiprotocol Label SwitchingGranularityRouting (electronic design automation)Computer networkDistributed computingLinear programmingMathematical optimizationQuality of serviceAlgorithmMathematics

Abstract

fetched live from OpenAlex

With the explosive traffic growth of WDM-based transport networks, the development of GMPLS (or multi- granularity)-based transport networks becomes essential to cope with the network scalability problems. This paper defines a novel problem of planning realistic GMPLS-based transport networks by (1) considering the whole traffic hierarchy defined in GMPLS; (2) allowing optical signal conversion at all granularity levels; (3) imposing optical reach constraint on the length of all- optical paths. We will call such a problem the routing and multi- granular paths assignment (RMGPA). The objective of the problem is to minimize the weighted port count in the transport network. Due to the computational complexity of the problem, only very-small-sized problems can be solved exactly through mixed integer linear programming (MILP) optimization. In this work, we propose novel heuristics that are capable of solving large-sized problems in a reasonable amount of time.

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 score0.709
Threshold uncertainty score0.260

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 head0.217
Teacher spread0.208 · 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
Published2007
Admission routes1
Has abstractyes

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