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Record W2137464581 · doi:10.1109/ccece.1998.682776

A novel scheduling scheme for serving VBR-encoded multimedia streams

2002· article· en· W2137464581 on OpenAlexaff
Anil Kumar Gupta, Xin Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCaching and Content Delivery
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceVariable bitrateScheduling (production processes)Quality of serviceComputer networkScheme (mathematics)Fraction (chemistry)Constant bitrateReal-time computingMultimediaDistributed computingMathematical optimization

Abstract

fetched live from OpenAlex

We propose a novel scheduling scheme, in which a new user is admitted for service by a multimedia server only if the service requirements of all the existing users and the new user can be satisfied. Each user is assumed to be requesting a variable bit rate (VBR) MPEG2 encoded media stream. The scheduling scheme maximizes the number of streams that can be served concurrently. The quality of service is guaranteed in terms of the fraction of the frames that may be dropped for a user. When a new request arrives, the admission control algorithm estimates the fraction of frames that may have to be dropped for the users assuming that the new request is accepted. If the fraction of frames is more than the drop rate committed to the users, the request is rejected, otherwise it is accepted. Our scheme is much more simpler than the schemes suggested in the literature and results in better utilization of the resources, and at the same time provides guaranteed QoS to the users requesting predictive service. Extensive simulations have been conducted to demonstrate the effectiveness of this scheduling scheme.

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: Methods · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.482

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)0.0000.000

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 head0.051
GPT teacher head0.239
Teacher spread0.188 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

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