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Record W1578881596

Maximizing access to IT services on resilient optical grids

2011· article· en· W1578881596 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Conference on Ultra Modern Telecommunications · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceComputer networkDimensioningNode (physics)Network topologyBandwidth (computing)GridDistributed computingEngineering
DOInot available

Abstract

fetched live from OpenAlex

An optical grid network provides high speed communications for large scale applications and services may require, for (very) limited time periods, an ultra-high bit rate network services at the order of the transmission capacity of the network infrastructures. In the context of resilient optical grids, we investigate how to maximize the grade of services for given transport capacities, while maximizing the protection level, i.e., against single link vs. single link and node (including server node) failures. In this paper, we present a large scale optimization model, solved with the help of Column Generation (CG) techniques. The model allows the exploration of different protection schemes: protection against single link failures, or single link and node failures, including or not the single failures of the server nodes. Numerical results are presented on some European and German network topologies. Results show that, for given transport capacities, guaranteeing protection against single link and node failures, or only against single link failures does not affect significantly the grade of services. Results also show that the link dimensioning which is proposed is fairly efficient in terms of bandwidth utilization.

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.

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: Empirical · Consensus signal: none
Teacher disagreement score0.845
Threshold uncertainty score0.893

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 science0.0020.000
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 head0.096
GPT teacher head0.322
Teacher spread0.226 · 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