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Record W2118703723 · doi:10.1109/glocom.2010.5683724

Design and Evaluation of an Intelligent Rate Controller for Heterogeneous Networks

2010· article· en· W2118703723 on OpenAlexaff
Jungang Liu, Oliver Yang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceRouterBottleneckBandwidth (computing)Network congestionQueueFuzzy logicController (irrigation)Queueing theoryComputer networkReal-time computingNetwork packetEmbedded system

Abstract

fetched live from OpenAlex

This paper proposes an intelligent rate controller for the Internet traffic. Called the IntelRate (Intelligent Rate), it is a router-based controller and uses the fuzzy logic control approach to adjust the source sending rate based on the instantaneous queue size of the router. Unlike other explicit congestion control protocols, the IntelRate controller need not evaluate the link states (such as bottleneck bandwidth or the number of flows in the link) while performing well to the sudden changes in traffic load or bandwidth. Simulation results and comparisons have verified the effectiveness of our scheme, and showed the IntelRate controller is more stable and robust upon large network changes than other existing protocols that rely on the estimation of available bandwidth in order to compute the admissible source sending rate.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.908
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.031
GPT teacher head0.278
Teacher spread0.247 · 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.

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

Citations3
Published2010
Admission routes1
Has abstractyes

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