Improved method for survivable network design based on pre-cross-connected trails
Bibliographic record
Abstract
Previous work developed the concept of 'pre-cross-connected trails' (PXTs), which are fully pre-connected linear structures of spare capacity used to protect one or more paths end-to-end. To date, the only approach for designing PXT-based restorable networks is a heuristic algorithm suited for the dynamic protection of demands as they arrive in a network. The heuristic can also be used as a 'green fields' planning algorithm for a known set of demands by running through the set and protecting them in order. In both cases, however, recent work has shown that the resulting PXT structures can be looping, as well as long and complex. While the capacity efficiency of the designs was high, the practicality of using such convoluted structures in any real network is doubtful. In this work we propose a semi-heuristic approach based on integer linear programming methods that allows important properties of the PXTs (such as length and degree of looping) to be tightly controlled. We also show how this method may be adapted to the dynamic protection of incrementally arriving random demands. Results show that even when PXTs are restricted to be totally non-looping and of much lower maximum length, we still attain capacity efficiencies near those of the original PXT design heuristic. A notable extra finding is that, in an efficient PXT network design in general, many PXTs are equivalent to standalone 1+1 APS arrangements for certain demand flows.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.005 | 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".