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Record W1788576967 · doi:10.1109/iscc.1999.780802

A traffic envelope and transmission schedule computation scheme for VoD systems

2003· article· en· W1788576967 on OpenAlexafffund
Fulu Li, Ioanis Nikolaidis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceStatistical time division multiplexingMultiplexingBandwidth (computing)Computer networkScheme (mathematics)MulticastScheduleReal-time computingHeuristicBandwidth allocationComputationDistributed computingAlgorithmTelecommunications

Abstract

fetched live from OpenAlex

Several proposals for video-on-demand (VoD) systems assume batching of the user requests and subsequent multicasting of the selected videos. We propose that the waiting time, which is essential for the sake of batching, can be overlapped with a more elaborate call admission scheme. The call admission scheme provides sufficient bandwidth at any point in time, in order to achieve guaranteed delivery of the video stream without any buffering or additional delays. The call admission is based on the construction of deterministic time-dependent envelopes. Essentially, statistical multiplexing is replaced by deterministic multiplexing. Bandwidth efficiency is maintained through the particular envelope construction which minimizes the amount of over-allocated bandwidth. The construction of the envelopes can be performed using either a computationally intensive exact algorithm or by a fast heuristic. Both the envelope construction and the call admission procedure are presented and simulated in detail. The results indicate the feasibility, benefits and tradeoffs of the proposed 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.230
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2003
Admission routes2
Has abstractyes

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