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Record W2098734823 · doi:10.1109/icc.1994.368951

End-to-end performance in ATM networks

2002· article· en· W2098734823 on OpenAlex
Jing-Fei Ren, J.W. Mark, J.W. Wong

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsUniversity of Waterloo
FundersGovernment of Canada
KeywordsComputer scienceTraverseNode (physics)Markov chainSmoothingMarkov processComputationCharacterization (materials science)Computer networkMarkov modelEnd-to-end principleAlgorithmMathematicsMachine learningEngineering

Abstract

fetched live from OpenAlex

We are concerned with the end-to-end performance of an ATM network. The focus is on the characterization of the smoothing effect on the individual traffic streams as they traverse the network nodes. We develop an analytical model and a method of analysis for a generic node. For Markov modulated on-off streams, it is found that the on period gets lengthened as the stream passes through each node. The characterization of the lengthening effect allows a departure stream to be approximated by another Markov modulated on-off stream with modified parameters. The method permits a recursive computation of performance at all the nodes in a tandem network representing an end-to-end connection. The accuracy of the approximate analysis is validated by comparing it with simulation results.< <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.

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)0.000
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 score0.951
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.012
GPT teacher head0.193
Teacher spread0.181 · 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

Quick stats

Citations21
Published2002
Admission routes2
Has abstractyes

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