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A Multi-core Multicast Approach for Delay and Delay Variation Multicast Routing

2017· article· en· W2785996293 on OpenAlexaff
Hovhaness A. Harutyunyan, Meghrig Terzian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsConcordia University
Fundersnot available
KeywordsMulticastComputer scienceXcastProtocol Independent MulticastComputer networkSource-specific multicastDistance Vector Multicast Routing ProtocolDistributed computingPragmatic General MulticastInter-domainRouting (electronic design automation)End-to-end delay

Abstract

fetched live from OpenAlex

Multicast communication constrained by end-to-end delay and inter-destination delay variation is known as Delay and Delay Variation Bounded Multicast (DVBM). These constraints make the multicast communication real-time. In this paper, we propose a multi-core multicast approach to solve the DVBM problem. The proposed three-phase algorithm, Multi-core DVBM Trees (MCDVBMT), semi-matches group members to core nodes. The message is disseminated to group members using trees rooted at the designated core nodes. Simulation results show that when existing single-core based algorithms fail to construct a tree satisfying both constraints, MCDVBMT succeeds using multiple cores with better inter-destination delay variation and traffic concentration. However, the cost and the end-to-end delay of the communication is higher than that of single-core algorithms.

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 score0.984
Threshold uncertainty score0.613

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 studies0.0010.000
Scholarly communication0.0010.001
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 head0.047
GPT teacher head0.282
Teacher spread0.235 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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