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Record W2159708013 · doi:10.1109/cnsr.2006.37

MPLS-based Multicast Shared Trees

2006· article· en· W2159708013 on OpenAlexaff
Ashraf Matrawy, Yi Wei, Ioannis Lambadaris, Chung–Horng Lung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsCarleton University
Fundersnot available
KeywordsMulticastProtocol Independent MulticastXcastComputer scienceSource-specific multicastPragmatic General MulticastComputer networkDistance Vector Multicast Routing ProtocolIP multicastInter-domainDistributed computingMulticast addressMultiprotocol Label SwitchingQuality of service

Abstract

fetched live from OpenAlex

This paper presents a study of our proposed architecture for the setup of a multipoint-to-multipoint (MP2MP) label switched path (LSP). This form of LSP is needed for establishing uni-directional multicast shared trees. Such trees are required for information distribution in applications such as video conferencing. The presented architecture is intended for multicast applications within a single autonomous domain and can be extended to cover inter-domain multicast sessions. We propose the use of one (or more) control points in the network called Rendez-vous points (RP) in a simple extention may utilize more than one RP to implement RP failure the PIM-SM protocol to implement multicast in MPLS networks. This architecture has the advantage of using existing MPLS techniques and existing routing protocols and requires only the addition of more management capabilities at the RPs. The experiments we carried out show that while retaining the advantages of using MPLS over traditional multicast routine, the performance of the new architecture is comparable to that of IP multicast in terms of the volume of control messages and label and memory consumption. Also the architecture scales well with the increase of the number of senders within a multicast group and with the increase of the number of multicast groups.

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 score0.957
Threshold uncertainty score0.286

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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

Citations10
Published2006
Admission routes1
Has abstractyes

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