Combined node and span protection strategies with node-encircling p-cycles
Bibliographic record
Abstract
We develop three new design models for combined node and span protection with a single set of node-encircling p-cycles (NEPCs). In a simple reference model each node is protected by a single dedicated NEPC. In the second model NEPCs can be shared by multiple nodes as needed, and each node is allowed to make use of as many different NEPCs as needed for a capacity-efficient design. To manage the complexity, protection routing is assumed to be evenly split in both directions around the NEPC, resulting in a slight over-provisioning of NEPCs and spare capacity. The final model is the most capacity efficient by virtue of also determining in which direction protection routing should proceed around each NEPC for each light-path affected by a node failure. The models can also permit a specified level of restorability below 100% so that a carrier could offer node-failure restorability on a differentiated service basis. A significant finding is that in test case networks 25% node restorability can be achieved in addition to 100% span restorability with a single integrated set of p-cycles that require as little as 4.1% more spare capacity than for conventional protection only against span-failures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".