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Record W2054816762 · doi:10.1002/dac.856

Design and implementation of a scalable switch architecture for efficient high‐speed data multicasting

2006· article· en· W2054816762 on OpenAlexaff
Cheng Li, R. Venkatesan, Howard M. Heys

Bibliographic record

VenueInternational Journal of Communication Systems · 2006
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMulticastComputer scienceComputer networkScalabilityDistributed computingCrossover switchUnicastModular designXcastProtocol Independent MulticastComputer architectureTelecommunicationsCrossbar switchOperating system

Abstract

fetched live from OpenAlex

Abstract This paper presents the design and implementation of a new scalable cell‐based multicast switch fabric for broadband communications. Using distributed control and modular design, the multicast balanced gamma switch features a scalable, high performance architecture for unicast, multicast and combined traffic under both uniform and non‐uniform traffic conditions. The important design characteristic of the switch is that a distributed cell replication function for multicast cells is integrated into the functionality of the switch element with the self‐routing and contention resolution functions. Thus, no dedicated copy network is required. In the paper, we discuss in detail the design issues associated with the multicast functionality of the switch using 0.18 µm CMOS technology and discuss the scalability of the switch in terms of architectural, implementation, and performance scalability. Synthesized results are provided for measures of circuit complexity and timing. Copyright © 2006 John Wiley & Sons, Ltd.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.904
Threshold uncertainty score0.308

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 science0.0020.000
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 head0.045
GPT teacher head0.328
Teacher spread0.283 · 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

Citations2
Published2006
Admission routes1
Has abstractyes

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