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Record W1561119721 · doi:10.1109/glocom.1994.513591

A dynamic priority queueing approach to traffic regulation and scheduling in B-ISDN

2002· article· en· W1561119721 on OpenAlexaff
Jing-Fei Ren, J.W. Mark, J.W. Wong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceQueueing theoryComputer networkNetwork packetPriority ceiling protocolScheduling (production processes)Priority inheritanceIntegrated Services Digital NetworkMultiplexingPriority queueDeadline-monotonic schedulingStatistical time division multiplexingReal-time computingPacket switchingFair queuingDynamic priority schedulingQueueRound-robin schedulingMathematical optimizationRate-monotonic schedulingQuality of serviceTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

A dynamic priority multiplexing scheme for traffic regulation and scheduling in a B-ISDN supporting widely diversified services is proposed. In this scheme, the instantaneous priority of a packet is given by the difference between a penalty reflecting the earliness of the packet arrival with respect to its targeted arrival time and a dynamically attained priority due to waiting in the buffer. The packet with the highest priority is scheduled for transmission when the link is available and the packet with the lowest priority is dropped when the buffer is full. When the priorities of all waiting packets increase linearly at rate one, the proposed scheme is shown to be equivalent to the virtual clock algorithm. For this special case, a simple expression for the per connection waiting time is obtained by using a heavy traffic approximation approach.

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 score0.862
Threshold uncertainty score0.357

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 head0.013
GPT teacher head0.213
Teacher spread0.200 · 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

Citations12
Published2002
Admission routes1
Has abstractyes

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