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Record W2115203306 · doi:10.1109/atm.1998.675111

Multipriority packet switching on the HYPER switch

2002· article· en· W2115203306 on OpenAlexaff
Hussein Alnuweiri, Yue He, M.R. Ito

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceComputer networkScheduling (production processes)Quality of serviceQueueScheme (mathematics)Asynchronous Transfer ModeNetwork packetQueueing theoryDistributed computing

Abstract

fetched live from OpenAlex

This paper develops an efficient buffer management scheme that makes generic ATM switches capable of supporting delay-sensitive as well as loss-sensitive traffic. The proposed scheme aims at enhancing the performance of ATM switches by maintaining the head cells of output queues in relatively short dedicated output buffers, while maintaining the long tails of overflowing queues in a shared-memory pool where various memory-space management schemes can be applied. Under this scheme, delay-sensitive (high-priority) cells can be forwarded immediately to the output buffers, where priority-based cell scheduling is exercised. Loss-sensitive (low-priority) cells are pushed into the shared-memory only if their output buffers are full. If the shared memory is full, then a suitable push-out scheme must be employed to provide fairness. We investigate the impact of various buffer management and cell scheduling policies on the dynamics of interaction among the two traffic classes. The results demonstrate the effectiveness of the proposed scheme in providing each traffic class with the required quality-of-service (QoS) performance over a wide range of traffic loads and buffer sizes.

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 categoriesInsufficient payload (model declined to judge)
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 score0.901
Threshold uncertainty score1.000

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 head0.046
GPT teacher head0.208
Teacher spread0.161 · 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.

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

Citations1
Published2002
Admission routes1
Has abstractyes

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