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Record W2058058900 · doi:10.1364/jon.3.000707

Priority scheme for supporting quality of service in optical burst switching networks

2004· article· en· W2058058900 on OpenAlexaff
Ayman Kaheel, Hussein Alnuweiri

Bibliographic record

VenueJournal of Optical Networking · 2004
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOptical burst switchingComputer networkQuality of serviceComputer scienceOffset (computer science)Scheduling (production processes)ProvisioningChannel (broadcasting)Service qualityReal-time computingDistributed computingService (business)EngineeringOptical performance monitoringWavelength-division multiplexing

Abstract

fetched live from OpenAlex

Feature Issue on Optical Interconnection Networks (OIN). We present what we believe to be a new scheme, called preemptive prioritized just enough time (PPJET), for quality-of-service (QoS) provisioning in bufferless optical burst switching (OBS) networks. PPJET provides better service for high-priority traffic by dropping reservations belonging to lower-priority traffic with a new channel-scheduling algorithm called preemptive latest-available unused channel with void filling (PLAUC-VF). An approximate method for calculating the dropping probability in PPJET is also discussed. Moreover, we perform discrete-event simulations to evaluate the performance of PPJET. Simulation results show that PPJET outperforms offset-based QoS schemes both in terms of dropping probability and end-to-end delay.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.306
Teacher spread0.279 · 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

Citations6
Published2004
Admission routes1
Has abstractyes

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