Failure-independent path protection with p-cycles: efficient, fast and simple protection for transparent optical networks
Bibliographic record
Abstract
Failure independent path-protecting (FIPP) p-cycles are an extension of the basic p-cycle concept which retains the property of pre-cross-connection of protection paths while acheiving end-to-end failure-independent path protection switching against either span or node failures. An issue with the current method of shared-backup path protection (SBPP) in a transparent optical network is that spare channels for the backup path must be cross-connected on-the-fly upon failure. It takes extra time and signaling to make the required cross-connections but, more importantly, until all connections are made it is not actually known if the backup optical path has adequate transmission integrity. Thus, pre-failure certainty about optical path integrity is an important reason to have backup paths fully pre-connected before failure. FIPP p-cycles support the same failure-independent, end-node activated switching of SBPP but with fully pre-connected protection paths. FIPP p-cycles may therefore be especially attractive for transparent optical networks. FIPP p-cycle network designs also exhibit capacity efficiency that is characteristic of path-oriented schemes. We think it is the only scheme known with these very high efficiencies, failure independence, and the property of fully pre-cross-connected protection paths.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".