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Record W2099972742 · doi:10.1364/jocn.5.000023

Design and Dimensioning of Logical Survivable Topologies Against Multiple Failures

2012· article· en· W2099972742 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

VenueJournal of Optical Communications and Networking · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsConcordia University
Fundersnot available
KeywordsDimensioningNetwork topologyComputer scienceComputer networkDistributed computingEngineering

Abstract

fetched live from OpenAlex

In IP-over-WDM networks, protection can be offered at the optical layer or at the IP layer. Today, it is well acknowledged that synergies need to be developed between the IP and optical layers in order to optimize the resource utilization and to reduce the costs and the energy consumption of future networks. In this paper, we study the design of logical survivable topologies for service recovery against multiple failures, including SRLG—shared risk link group—failures in IP-over-WDM networks. We propose a new optimization model, called surlog_cgilp, based on a column generation path formulation. It is highly scalable and allows the exact solution of several benchmark instances, which have only been solved with the help of heuristics so far. In the numerical experiments, we investigate the dimensioning of the physical links assuming IP restoration against multiple-link failures. We observe that the redundancy ratios (recovery over primary ratios for the bandwidth requirements) that are obtained are similar to the redundancy ratios reported for optical protection.

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)0.001
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: Empirical
Teacher disagreement score0.750
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.050
GPT teacher head0.267
Teacher spread0.217 · 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