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Record W1490328206 · doi:10.1109/icppw.2004.81

Traffic classification and fairness in wavelength routed optical networks

2004· article· en· W1490328206 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

VenueProceedings of the International Conference on Parallel Processing · 2004
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsReservationRouting and wavelength assignmentComputer scienceComputer networkHop (telecommunications)Blocking (statistics)WavelengthRouting (electronic design automation)Distributed computingConstraint (computer-aided design)Wavelength-division multiplexingMathematicsPhysicsOptics

Abstract

fetched live from OpenAlex

The wavelength continuity constraint on the routing and wavelength assignment algorithms gives rise to a serious problem of fairness due to which longer-hop lightpaths (4-hop, 5-hop) suffer from a higher blocking probability as compared to shorter-hop lightpaths. By using wavelength reservation we try to control the contention of resources. We investigate the impact of different wavelength assignment schemes and routing algorithms on the wavelength reservation. By using a combination of routing and wavelength assignment schemes with wavelength reservation we show in this paper that the fairness problem can be mitigated.

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: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.495

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 head0.028
GPT teacher head0.252
Teacher spread0.225 · 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