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Record W2151861861 · doi:10.1109/glocom.1994.513318

ATM switching node design based on a versatile traffic descriptor

2002· article· en· W2151861861 on OpenAlexaff
M. Vishnu, J.W. Mark

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBurstinessComputer scienceComputer networkQuality of serviceAsynchronous Transfer ModeTraffic shapingBandwidth (computing)Scheduling (production processes)Network traffic controlATM adaptation layerIntegrated Services Digital NetworkTraffic policingDistributed computingNetwork packetEngineering

Abstract

fetched live from OpenAlex

Because of the high speed, the provision of unrestricted bandwidth on demand by ATM-based B-ISDN can only be satisfied by sacrificing network resource utilization. In ATM networks, the network resources are link bandwidth and node buffer space. A new traffic descriptor which directly specifies the resource requirements of ATM traffic streams in terms of bandwidth, buffer space and a set of parameter adjustment rules is proposed. We call this a versatile traffic descriptor (VTD) because it can describe relevant characteristics of all ATM traffic streams including nonstationary traffic streams. Also, the VTD incorporates both the traffic description and the user's QoS specifications. Therefore, there is no need to specify the QoS requirements separately. A radically new, VTD-based switching node design, which uses novel per-connection buffering scheme and service scheduling scheme is also proposed. It is conjectured that the proposed earliest due date (EDD) service scheduling scheme is optimal in per-connection burstiness reduction.

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)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.923
Threshold uncertainty score0.762

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)0.0000.001

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 head0.032
GPT teacher head0.200
Teacher spread0.168 · 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

Citations4
Published2002
Admission routes1
Has abstractyes

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