Segment p-cycle design with full node protection in WDM mesh networks
Bibliographic record
Abstract
Segment p-cycles offer an interesting compromise between the classical (link) p-cycles and the path p-cycles (also known as FIPP p-cycles), inheriting most advantages of both p-cycle schemes. In their original form, segment p-cycles do not offer 100% node protection, i.e., do not guarantee any protection against node failure for the endpoints of the segments. Indeed, if we allow some p-cycle overlapping, it is possible to ensure 100% node protection: this is the focus of the present study. We propose a new efficient design approach for segment p-cycles, called segment Np-cycles, which ensure 100% protection against any single failure, either link or node (endpoints of requests are excluded). In order to evaluate the performances of segment Np-cycles, we develop a new optimization model based on column generation (CG) techniques. The use of such techniques eliminates the need to explicitly enumerate all segment Np-cycle configurations, but instead leads to a process where only improving segment Np-cycle configurations are generated. Numerical results demonstrate that segment Np-cycles are comparable, sometimes even more efficient, than path p-cycles with respect to their capacity requirement. In addition, in order to ensure 100% node protection, they only require a marginal extra spare capacity than the regular segment p-cycles.
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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) | 0.001 | 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".