Extending the p-cycle concept to path segment protection for span and node failure recovery
Bibliographic record
Abstract
The paper introduces an extension to the method of p-cycles for network protection. The p-cycle concept is generalized to protect path segments of contiguous working flow, not only spans that lie on the cycle or directly straddle the p-cycle. The original span protecting use of the p-cycle technique is extend to include path protection or protection of any flow segment along a path. It also gives an inherent means of protecting working flows that transit a failed node. We use integer linear programming to study the new concept and determine its inherent capacity requirements relative to prior p-cycle designs and other types of efficient mesh-survivable networks. Results show that path-segment-protecting p-cycles ("flow p-cycles") have capacity efficiency near that of the shared backup path-protection (SBPP) scheme currently favored for optical networking. Because its protection paths are fully preconnected and because it protects path segments (not entire paths), it has the potential for both higher speed and higher availability than SBPP. We also develop capacity optimization models to support 100% restoration of transiting flows through failed nodes. Only a very small additional spare capacity is needed to achieve both 100% span and intermediate node-failure restorabilities, and a very high transiting traffic restorability can be accomplished for node failure restorability given spare capacity only for span-failure protection. An immediate practical application is to suggest the use of flow p-cycles to protect transparent optical express flows through a regional network.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".