Toward jointly optimized design of failure-independent path-protecting p-cycle networks
Bibliographic record
Abstract
Failure-independent path-protecting (FIPP) p-cycles are an extension of the basic concept of p-cycles to provide an end-to-end path-protecting mechanism. Initial work showed that FIPP p-cycle networks are comparable in capacity efficiency to existing methods of shared-backup path protection but employ fully pre-cross-connected protection paths. This makes FIPP p-cycles as fast as conventional span protecting p-cycles, and much faster than shared-backup path protection where spare channels must be assembled into protection paths in real time following a failure. The property of full pre-cross-connection of the protection structures is also an attractive advantage in transparent optical networking, where on-the-fly transparent concatenation of wavelength channels to form protection paths may not provide an adequate a priori assurance of optical path integrity. Thus, there are several motivations for interest in this new concept. Prior work on this topic has, however, considered only the design of FIPP p-cycle networks where the working demands are routed before decisions are made about the protection structures. Here we consider the problem of jointly optimized FIPP p-cycle network design and provide some insights, guidelines, and a semiheuristic procedure for coordinated routing of working demands and the generation of the protection structures.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".