<title>Design-survivable WDM networks using a path protection algorithm</title>
Bibliographic record
Abstract
A major challenge of survivable wavelength-division multiplexing (WDM) network design is deciding how much spare capacity there should be and where it should be placed, so that interrupted traffic can be recovered within a very short time. Protection, including link protection and path protection, is the main way to solve this problem and to prevent huge losses. Link protection and dedicated path protection currently dominate this fault recovery field. Both have short recovery time and low resource efficiency. Shared path protection is also resource efficient with acceptable recovery time. More and more industries recognize that shared path protection is the trend and are trying to find good methods to design shared protection paths. This paper proposes a new efficient algorithm-Predetermined Protection Path Design (PPD), which can design maximum sharing protection paths. We describe in detail how PPD works, and compare it with the existing protection algorithms. Simulation results show that PPD can design maximum sharing protection paths and achieve 28% better resource efficiency than dedicated path protection as well as 12% better resource efficiency than the current shared path protection algorithms, with acceptable recovery time. PPD also has good generality, scalability and restorability.
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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 | 0.001 |
| 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) | 0.005 | 0.002 |
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".