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Record W2138116305 · doi:10.1109/conect.2002.1039252

Feedback output queuing: a novel architecture for efficient switching systems

2003· article· en· W2138116305 on OpenAlexaff
Victor Firoiu, E. Gunduzhan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsComputer scienceQuality of serviceScalabilitySpeedupQueueing theoryComputer networkNetwork packetPacket switchingDistributed computingParallel computing

Abstract

fetched live from OpenAlex

We consider the problem of providing QoS guarantees in a high-speed packet switch. As basic requirements, the switch should be scalable to high speeds per port, a large number of ports and a large number of traffic flows with independent QoS guarantees. Existing scalable solutions are based on virtual output queueing, which is computationally complex when required to provide QoS guarantees for a large number of flows. We present a novel architecture for packet switching that provides support for such QoS guarantees. A cost effective fabric with small external speedup is combined with a feedback mechanism that enables the fabric to be virtually lossless, thus avoiding packet drops indiscriminate of QoS flows. Through analysis and simulation, we show that this architecture provides accurate QoS support, has low computational complexity and is scalable to very high port speeds.

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)0.001
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 score0.969
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.024
GPT teacher head0.237
Teacher spread0.213 · 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

Citations3
Published2003
Admission routes1
Has abstractyes

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