A New Insight and Approach to Node Failure Protection with Ordinary p-Cycles
Bibliographic record
Abstract
Since the advent of p-cycles it has been understood that, in addition to their span-protecting properties, p-cycles have the same inherent protection as a BLSR ring to "on-cycle" demands that transit a failed node. It has remained less clear how to protect straddling demand flows from node failure as well. Other work has sought to achieve full node protection with extensions of the p-cycle concept such as node-encircling p-cycles, path-segment or "flow"-protecting p-cycles, and failure independent path-protecting p-cycles. Here we report a rather simple and useful finding about the problem-that high levels of node failure protection can be achieved with an ordinary set of p- cycles which designed in a way that every demand that transits a node in a straddling manner is also intercepted at points upstream and downstream on its route by some other p-cycle. We first characterize the inherent properties of ordinary minimum- capacity p-cycle network designs when inspected for use from this new standpoint. We then alter the basic network design model to maximize node restorability and even achieve 100% protection against both single node and span failures, with little additional capacity. The practical importance is that the simplicity of basics-cycles is retained, and only one set ofp-cycles is required, while efficiently achieving node failure protection either for priority paths or for all demands in a network.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".