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Record W2000917981 · doi:10.5539/mas.v9n3p90

Delay Measurement in Openflow-Enabled MPLS-TP Network

2015· article· en· W2000917981 on OpenAlexvenueno aff
Mounir Azizi, Rédouane Benaini, Mouad Ben Mamoun

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldComputer Science
TopicSoftware-Defined Networks and 5G
Canadian institutionsnot available
Fundersnot available
KeywordsMultiprotocol Label SwitchingOpenFlowComputer scienceComputer networkKey (lock)Software-defined networkingController (irrigation)Quality of serviceComputer security

Abstract

fetched live from OpenAlex

The present article is aimed to show how we can take profit from similarities between Multiprotocol Label Switching Transport Profile (MPLS-TP) architecture and Software Defined Network SDN implementation. We will outline how the SDN model can be useful for MPLS-TP network and how to meet Transport carrier requirements. We are also proposing an operational model able to satisfy one of the important Key Performance Indicators KPI related to Delay by using Delay Measurement’s DM operation and maintenance OAM procedure. This concept shows how real traffic engineering is done based on DM application module on top of Openflow controller.

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)0.005
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.943
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0030.001
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 head0.056
GPT teacher head0.244
Teacher spread0.188 · 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
Published2015
Admission routes1
Has abstractyes

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