Characterization of pre-cross-connected trails for optical mesh network protection
Bibliographic record
Abstract
Recently there has been interest in so-called pre-cross-connected protection architectures for optical networks. The main benefit of pre-cross-connected protection is that multiple cross-connection actions are not required in real time at the time of failure. This addresses the practical concern that, in a transparent optical network, one may not be able to make a series of protection path-forming cross-connections in a succession of optical spans with certainty that the resultant end-to-end connection has optical path integrity. Self-healing rings, p-cycles, and preconnected linear segment protection are examples of prior methods that employ prefailure cross-connection of protection capacity but are not end-to-end path-oriented. More recent work has proposed pre-cross-connected trails (PXTs), which are fully preconnected linear path-protecting structures. The same work also provided an online heuristic algorithm for generating PXT network designs. However, important and interesting properties such as length and cyclicity of the PXT structures remained to be characterized. We delve further into PXT network design, attempting to validate claims made previously and to understand the structural and operational properties of PXTs. This involves reimplementation of and experimentation with the above heuristic. Results show that heuristically obtained designs frequently contain PXTs of great total length and high complexity, as well as other PXTs that are equivalent to 1+1 automatic protection switching (APS) arrangements. Through diagramming and statistical analysis of PXT characteristics we give the first intuitive appreciation of the structure and function of PXTs.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".