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Record W2465969241 · doi:10.1109/ict.2016.7500475

Improving carrier ethernet recovery time using a fast reroute mechanism

2016· article· en· W2465969241 on OpenAlexaff
Marcelo F. Santos, Jean‐Charles Grégoire

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsCarrier EthernetConnection-oriented EthernetMetro EthernetEthernet over SDHComputer networkEthernet over PDHComputer scienceEthernet flow controlATA over EthernetSynchronous EthernetEthernetIndustrial EthernetLocal area networkDistributed computing

Abstract

fetched live from OpenAlex

Ethernet has evolved from a local network technology to a technology that can be used in metro access and transport networks. Unfortunately the new Ethernet networks cannot achieve the level of reliability of traditional TDM Carrier-class networks. In order to improve Ethernet performance, we need to develop new mechanisms specifically adapted to this type of network technology. The high convergence time of control protocols is one of the problems in both traditional and new generation Ethernet networks. This paper presents a fast recovery mechanism as a solution for the high convergence time. This mechanism uses tunnels and cycles to provide a local recovery and is adapted to a Carrier-class Ethernet network controlled by a link-state protocol. Simulations are used to show the advantage of the use of this mechanism in comparison to the global recovery mechanism of link-state protocols.

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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.197
Teacher spread0.189 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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