<title>Mesh-restorable networks with complete dual failure restorability and with selectively enhanced dual-failure restorability properties</title>
Bibliographic record
Abstract
We consider extensions of the most common mesh-restorable network capacity design formulation that enhance the dual-failure restorability of the designs. A significant finding is that while design for complete dual-failure restorability can require triple the spare capacity, dual failure restorability can be provided for a fairly large set of priority paths with little or no more spare capacity than required for single-failure restorability. As a reference case we first study the capacity needs under complete dual-failure restorability. This shows extremely high capacity penalties to support 100% dual-failure restorability. A second design model allows a user to specify a total capacity budget limit and obtain the highest average dual-failure restorability possible for that investment limit. A third design strategy supports multiple-restorability service class definitions at minimum total cost. Restorability can range from best-efforts-only on any failure to an assurance of complete single and dual-failure restorability, on a per-demand basis. This work shows how to economically support an added service class in the upward quality direction: assured dual failure survivability. This lets a network operator tailor the investment in capacity to provide ultra-high availability on a selective basis, while avoiding the very high investment required for complete dual-failure restorability for all.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".