Theoretical underpinnings for the efficiency of restorable networks using preconfigured cycles ("p-cycles")
Bibliographic record
Abstract
Previous work on restorable networks has shown experimentally that one can support 100% restoration with an optimized set of closed cycles of spare capacity while requiring little or no increase in spare capacity relative to a span-restorable mesh network. This is important and unexpected because it implies that future restoration schemes could be as capacity efficient as a mesh network, while being as fast as ring-based networks because there is no real-time work at any nodes other than the two failure nodes. This paper complements the prior work by giving a greater theoretical basis and insight to support the prior results. We are able to show in a bounding-type of argument that the proposed protection cycles ("p-cycles") have as high a restoration efficiency as it is possible to expect for any type of preconfigured pattern, and are categorically superior to preconfigured linear segments or trees. We are also able to show that the capacity efficiency of a fully preconfigured p-cycle network has the same well-known lower bound as that of a span restorable mesh network which is cross-connected on-demand. These results provide a theoretical underpinning for the efficiency of p-cycles and confirmation of the experimental observations.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".