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Record W2121778557 · doi:10.1109/iscc.2001.935365

A scheme for QoS-based dynamic multicast routing

2002· article· en· W2121778557 on OpenAlexaff
Ali M. Roumani, Hossam S. Hassanein

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsQueen's University
Fundersnot available
KeywordsMulticastComputer networkComputer scienceDistance Vector Multicast Routing ProtocolProtocol Independent MulticastXcastSource-specific multicastDistributed computingPragmatic General MulticastMulticast address

Abstract

fetched live from OpenAlex

Multimedia applications involving real-time audio and/or video transmissions require strict QoS constraints (end-to-end delay bound, bandwidth availability and loss probability) to be met by the network. To guarantee real-time delivery of multimedia packets, a multicast channel needs to be established in advance using a path selection algorithm that takes into account the QoS constraints. In this paper we describe a distributed destination-controlled multicast routing (DCMR) protocol for multicasting with dynamic membership. DCMR establishes multicast connections in two stages, a forward routing stage, where setup messages carrying routing information are forwarded to the new destination, and a backward configuration stage, where the destination chooses a path with the highest available bandwidth under the delay constraint. Reservation is then attempted along this path backwards towards the multicast tree. Our simulations show that DCMR outperforms existing multicast routing algorithms in terms of call acceptance and connection setup time as it achieves low call blocking ratios.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.020
GPT teacher head0.236
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

Citations0
Published2002
Admission routes1
Has abstractyes

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