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Record W2142368954 · doi:10.1109/ccece.2007.91

End-to-End Packet Loss Constrained Routing and Admission Control for MPLS Networks

2007· article· en· W2142368954 on OpenAlexaff
Desire Oulai, Steven Chamberland, Samuel Pierre

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsPolytechnique MontréalAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsMultiprotocol Label SwitchingComputer networkComputer scienceQuality of serviceAdmission controlVirtual routing and forwardingNetwork packetRouting protocolRouting (electronic design automation)End-to-end principlePacket lossStatic routing

Abstract

fetched live from OpenAlex

Dynamic connection admission control (CAC) is an important mechanism to guarantee end-to-end quality of service (QoS) for specific Internet protocol (IP) traffic flows in multiprotocol label switching (MPLS) networks. Most of the solutions proposed in the literature target to satisfy the QoS constraints for the new request without considering them for the already admitted flows in the network. In this paper, we propose a joint routing and admission control mechanism for the IP traffic flows in MPLS networks. The solutions are obtained by solving exactly a mathematical programming model including end-to-end packet loss constraints for all traffic flows. Numerical results show that the model can be solved rapidly even for real-size instances of the problem.

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 score0.858
Threshold uncertainty score0.621

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 head0.009
GPT teacher head0.242
Teacher spread0.233 · 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

Citations1
Published2007
Admission routes1
Has abstractyes

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