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Record W2099711142 · doi:10.1109/wescan.1993.270567

Integration of telecommunications switching on SCS hypercube packet switches

2002· article· en· W2099711142 on OpenAlexaff
Carl McCrosky, Iain M. Reid

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsComputer scienceBandwidth (computing)Computer networkNetwork packetScalabilityHypercubeQuality of serviceInterconnectionDynamic bandwidth allocationDistributed computingPacket switchingParallel computingOperating system

Abstract

fetched live from OpenAlex

A switch architecture is proposed which can integrate a wide range of modern service demands within one architecture and one physical realization. The proposed architecture is based on highly parallel, distributed packet interconnection networks. Hypercube-based parallel packet switches utilizing the saturated constant shuffle (SCS) algorithm appear to provide the possibility of unified services. The SCS algorithm ensures economical and highly scalable implementations. The aggregate bandwidth of these switches is available for a wide range of services. A great deal of flexibility in the allocation of this aggregate bandwidth is possible. Bandwidth can be reserved for constant bandwidth services such as telephones. Pools of bandwidth can be made available for pools of bursty traffic such as computer data; within these pools individual users can compare for bandwidth. Variable bandwidth traffic cannot consume bandwidth allocated to constant bandwidth users. The authors present the essential ideas of the proposed switch architecture, describe its performance potential, and discuss how the quality of the various services can be assured.< <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)0.000
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 score0.979
Threshold uncertainty score0.243

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 science0.0000.000
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 head0.053
GPT teacher head0.250
Teacher spread0.197 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

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