p-cycle network design with hop limits and circumference limits
Bibliographic record
Abstract
p-cycles offer an approach to protection of optical transport networks which is as fast as a ring-based network but with mesh-like capacity efficiency. One misconception about p-cycle designs seems to be that they involve long protection paths, even though it is trivial to limit the circumference of cycles admitted to the design problem. In addition, through straddling span considerations the average protection path on a p-cycle is actually shorter than in a corresponding ring. Nonetheless there are some open questions regarding path and cycle circumference limit effects with p-cycles. One question is whether p-cycle networks exhibit a "threshold hop-limit" effect corresponding to that well-known aspect of span-restorable mesh networks. (Beyond the threshold hop-limit there are negligible savings in capacity.) To study this question we extend the existing p-cycle network design theory to include the capability of direct restriction of protection path lengths, rather than indirect restriction through circumference limits. A second, quite practical question is to ask how well simple limitation of cycle circumferences serves as a surrogate for a more involved design method of directly asserting a hop (or distance) limit on the maximum length of protection paths. The answers to the questions and the methods developed to address them both enhance our ability to design p-cycle networks in which optically transparent length may affect transmission quality, or where the length of protection paths may affect cost if regeneration is required en route of a protection path. The main findings are that p-cycles do exhibit threshold hop-limiting effects (at about two or three hops above those in corresponding mesh networks) and that cycle limiting is a simple and effective surrogate for direct limitation on path lengths in p-cycle design problems.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".