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Record W2167952336 · doi:10.1364/jocn.1.00a219

Even Slot Transmission in Slotted Optical Packet-Switched Networks

2009· article· en· W2167952336 on OpenAlexaff
Akbar Ghaffarpour Rahbar, Oliver Yang

Bibliographic record

VenueJournal of Optical Communications and Networking · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceComputer networkNetwork packetBandwidth (computing)Transmission (telecommunications)Packet switchingAccess networkFrame (networking)Electronic engineeringTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

The issue of transmitting optical packets (called slots in this paper) smoothly toward an egress switch appears to be overlooked in bandwidth access schemes proposed for optical packet-switched (OPS) networks. Here, we explain the benefits of providing an even (smooth) slot transmission and propose four basic metrics and three hybrid metrics to achieve an even slot transmission from an ingress switch of a multiwavelength/fiber slotted OPS network. The even slot transmission can help improve network performance parameters required for Internet applications. An index parameter is also introduced for each method, and a formulation is provided to gauge how even a transmission is among different combinations of frame periods, wavelengths, and fibers. These formulations can provide us with a new approach to relatively compare different bandwidth access schemes in order to determine which scheme can provide an even transmission of traffic to an OPS network. An example is provided for the comparison of two existing bandwidth access schemes in terms of even transmission of traffic.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0000.001
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 head0.020
GPT teacher head0.263
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations4
Published2009
Admission routes1
Has abstractyes

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