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Record W2146888913 · doi:10.1109/mmcs.1999.778452

A fuzzy-based traffic controller for high-speed ATM networks using realistic traffic models

2003· article· en· W2146888913 on OpenAlexaff
A. Kasiolas, Dimitrios Makrakis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer scienceAsynchronous Transfer ModeQuality of serviceMultiplexingController (irrigation)Fuzzy logicStatistical time division multiplexingComputer networkLeaky bucketData stream miningReal-time computingData miningTelecommunications

Abstract

fetched live from OpenAlex

In modern ATM networks, usage parameter control (UPC) is necessary in order to ensure conformity of the incoming data streams to its negotiated parameters. This protects the quality of conforming applications from degradations that are due to additional network loading produced by nonconforming applications. The paper focuses on the congestion problem created in an ATM switch by the statistical multiplexing of various traffic sources. The objective is to achieve the maximum possible statistical multiplexing gain without violating the quality of service requirements of the individual streams. A fuzzy traffic controller is proposed, which performs policing and shaping of the input streams so that the above mentioned goals are satisfied. The controller is a fuzzy implementation of the two-threshold leaky bucket mechanism that is widely used in commercial ATM switches.

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)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.030
GPT teacher head0.240
Teacher spread0.210 · 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
GenreMethods

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
Published2003
Admission routes1
Has abstractyes

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