Maximizing access to IT services on resilient optical grids
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".