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Record W2097780935 · doi:10.1109/infcom.1997.644503

The single queue switch

2002· article· en· W2097780935 on OpenAlexaff
Masoud Reza Hashemi, Alberto Leon‐Garcia

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceQueueing theoryQueueScheduling (production processes)Crossover switchComputer networkAsynchronous Transfer ModeParallel computingDistributed computingCrossbar switchEngineeringTelecommunications

Abstract

fetched live from OpenAlex

In this paper we introduce a new approach to ATM switching. We propose an ATM switch architecture which: uses only a single shift-register-type buffering element to store and queue the cells; and within the same physical queue switches the cells by organizing them in logical queues destined to different output lines. The buffer is also a sequencer which allows flexible ordering the cells in each logical queue to achieve any appropriate scheduling algorithm. This switch is proposed for use as the building block of large-scale multi-stage ATM switches and as the scheduler/controller for the RAM-based switches. The single queue switch implements output queueing and performs full buffer sharing. The hardware complexity is low. The number of input and output lines can vary independently without affecting the switch core. The size of the buffering space can be increased by simply cascading the buffering elements to each other.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.004

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 head0.027
GPT teacher head0.194
Teacher spread0.167 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations2
Published2002
Admission routes1
Has abstractyes

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