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Record W2027522046 · doi:10.1109/wocn.2007.4284130

Periodically Scheduled Burst Flows in Optical Burst Switching Networks

2007· article· en· W2027522046 on OpenAlexaff
Lei Li, Srinivas Sampalli

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsOptical burst switchingComputer scienceComputer networkQuality of serviceMultiprotocol Label SwitchingReservationOffset (computer science)Label switchingBandwidth (computing)Burst switchingOptical switchConstruct (python library)Wavelength-division multiplexingOptical performance monitoringElectronic engineeringWavelengthEngineeringNetwork packetTransmission delay

Abstract

fetched live from OpenAlex

Providing flexible quality of service (QoS) for multiple classes of traffic is one of the important challenges in the design of optical burst switching (OBS) networks. In this paper, we propose a novel bandwidth reservation scheme for periodically scheduled bursts. Multi-protocol label switching (MPLS) is used to construct reliable burst delivery channels across the network. Meanwhile, best effort bursts are transferred using one way offset signaling to improve the wavelength efficiency, Our approach provides flow level QoS control without compromising the performance of best effort bursts. Simulation results show that our approach is better than early dropping schemes.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.455
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.226
Teacher spread0.219 · 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.

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

Citations1
Published2007
Admission routes1
Has abstractyes

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