Design of path-segment-protecting p-cycles in survivable WDM mesh networks
Bibliographic record
Abstract
Survivability is an essential feature in the design of WDM mesh networks for continuous service delivery in the case of failures. Segment p-cycles (also known as flow p-cycles) offer an interesting protection approach with a good trade-off between protection capacity cost and recovery speed. In this paper, we propose a new design method for segment p-cycles based on a large scale optimization tool, namely column generation techniques (CG). In contrast with the conventional design approaches which pre-enumerate the candidate segment p-cycles and establish their protection relationships with the protected capacity, our CG based optimization approach dynamically generates segment p-cycles with their protection capabilities during the optimization process. Computational results show that our design approach of segment p-cycles is much more capacity efficient and scalable than the prevalent design approach in the literature. A saving of protection capacity in the range of 3% to 20% is achieved. In addition, our CG-based design is highly scalable, and much faster in large dense networks than the prevalent classical ILP-based optimization approach.
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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 | 0.000 |
| Open science | 0.000 | 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".