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Record W1974179724 · doi:10.1117/12.509441

Performance evaluation of a new fairness control scheme for ring networks with spatial reuse

2003· article· en· W1974179724 on OpenAlexaff
Helen Tang, Ioannis Lambadaris

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2003
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceComputer networkReuseQueueing theoryQueueBandwidth (computing)Node (physics)Ring networkNetwork packetThroughputScheme (mathematics)Distributed computingNetwork topologyWirelessTelecommunicationsEngineeringMathematics

Abstract

fetched live from OpenAlex

We consider a ring in which simultaneous transmission of messages by different stations is allowed, a property referred to as spatial reuse. A ring network with spatial reuse can achieve a network level throughput much higher than the channel rate. A widely used scheme to achieve spatial reuse is Buffer Insertion Ring (BIR). However, because non-preemptive priority is given to the ring traffic, BIR scheme can lead to fairness problems in distributing the ring bandwidth among distinct nodes. In this paper, we propose a novel approach that provides fair access to all nodes and features low complexity. Within each node, the proposed approach allocates a separate queue for every upstream node. Each queue receives its fair share of the ring bandwidth based on an assigned weight value. Performance of the proposed scheme in terms of fairness and average packet delay has been evaluated through both simulations and analysis. The results show that the new scheme called <i>Source-Based Queuing</i> (SBQ) can provide fairness with less end-to-end delay compare to the BIR scheme.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.527
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdvanced Optical Network TechnologiesFrench-language works237,207