PWCE design in survivablewdm networks using unrestricted shape p-structure patterns
Bibliographic record
Abstract
We propose a new way to design a Protected Working Capacity Envelope (PWCE) in survivable WDM networks by using pre-configured protection structures with unrestricted shapes (arbitrary shape patterns). So far, the pre-configured cycle (p-cycle) structure is the only building block that has been used in the design of PWCEs. In this paper, we do not impose any restriction on the shapes of the protection building blocks, rather, we search for p-structures in the network that favor the protection efficiency (reliability, redundancy) and recovery delay (local recovery). In order to cope with the large solution space, we use an efficient large scale optimization technique that relies on Column Generation (CG) where only globally most promising p-structures are enumerated on the fly during the optimization process. We compare the capacity efficiency, the reliability, and the average length of the backup paths of our PWCE design approach with the p-cycle based one. The results show that a design based on unrestricted p-structure patterns is ~10% less capacity redundant, ~15% more reliable, and allow recovery along shorter backup paths compared to the p-cycle based scheme.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".