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Record W2157909282 · doi:10.1109/pacrim.1991.160771

A hierarchical bandwidth management architecture for solving bandwidth congestion in the ATM/B-ISDN user-network interface

2002· article· en· W2157909282 on OpenAlexaff
Oliver Yu, Victor C. M. Leung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer networkNetwork congestionComputer scienceIntegrated Services Digital NetworkNetwork traffic controlAsynchronous Transfer ModeBandwidth managementNetwork architectureDistributed computingBandwidth (computing)

Abstract

fetched live from OpenAlex

The authors describe a methodological framework for the analysis of network congestion in a generic asynchronous transfer mode (ATM)/broadband-ISDN (B-ISDN) network. A three-level hierarchical network congestion management architecture is proposed; with a higher hierarchical level corresponding to a slower congestion control frequency. By dividing the congestion problem into scopes according to the control frequencies, the architecture incorporates isolated, distributed, and centralized control models into the corresponding congestion scopes and then applies appropriate control schemes. Within this architectural framework, the authors focus on the hierarchical congestion management schemes at the UNI (user-network interface) access node which allows an integrated access of heterogeneous user traffic types; and which allows its output links to the network to be subjected to random disruptions due to internode congestion control exerted by the immediate neighboring network nodes.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.226
Teacher spread0.212 · 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
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
Published2002
Admission routes1
Has abstractyes

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