A new approach to node-failure protection with span-protecting p-cycles
Bibliographic record
Abstract
Recent work has revealed a new, relatively simple and possibly cost-effective, approach to achieve combined protection of optical networks against both node and span failures. The resulting network designs use only a single set of p-cycle structures that have the same or only slightly more capacity than a corresponding optimal set of p-cycles for span protection. The new principle is based on a generalization of how nodes in a BLSR-ring or p-cycle (to date) derive survivability through loop-back at the nearest two neighbour-nodes on the same ring. The generalization views any combination of node failure and an affected transiting path from the standpoint of the two-hop segment defined by the failure node, and the nodes immediately adjacent on the affected path. We then ask whether these nodes are found together within the same p-cycle as the failure node, or another p-cycle entirely. In any case where they are, we show that the transiting path affected by the node failure is inherently restorable by ordinary p-cycle switching actions whether the respective two-hop segment is on-cycle, straddling, or partially on-cycle and partially straddling. We explain the principle and characterize its effectiveness in terms of network-wide single node failure restorability (R1-node) in networks designed only for minimum spare capacity, networks designed for enhanced R1-node(at min capacity) and networks designed strictly for R1-node= 1.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".