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Record W1682274164 · doi:10.1109/glocom.2001.966246

Efficient fair queuing with decoupled delay-bandwidth guarantees

2002· article· en· W1682274164 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceBandwidth allocationScheduling (production processes)Bandwidth (computing)Queueing theoryEstimatorQueuing delayComputer networkDynamic bandwidth allocationUpper and lower boundsWeighted fair queueingGeneralized processor sharingFair queuingReal-time computingRound-robin schedulingDynamic priority schedulingMathematical optimizationQuality of serviceMathematics

Abstract

fetched live from OpenAlex

In this paper we introduce a new scheduling system with decoupled delay bound and bandwidth allocation guarantees. It is based on an existing efficient fair scheduler, Frame-based Fair Queueing (FFQ), and therefore inherits the fairness, simplicity and efficiency of that policy. We combine a slightly modified version of an FFQ scheduler with the link sharing concept using rate estimator modules, in order to provide the capability of assigning delay bound and bandwidth allocation to each traffic class independently. This is achieved by defining two sets of rates, one for normal operation of FFQ which defines the delay bound of each class and the other for assigning bandwidth allocation to each class. In normal situations the system acts as a normal FFQ but if a class misbehaves, the system reacts and prevents that class from degrading delay and bandwidth of other classes.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.804
Threshold uncertainty score0.495

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 head0.005
GPT teacher head0.167
Teacher spread0.162 · 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

Quick stats

Citations4
Published2002
Admission routes1
Has abstractyes

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