A disjoint route-sets approach to design of path-protecting p-cycle networks
Bibliographic record
Abstract
Recent work has proposed the concept of failure-independent path-protecting (FIPP) p-cycles as a pre-connected, failure independent, path-protecting network architecture [A. Kodian, W. D. Grover (2005)]. FIPP p-cycles extend p-cycles by adding the property, like shared backup path protection (SBPP), of providing end-to-end failure independent path switching against either span or node failures. Especially in a transparent or translucent optical network, the property of pre-cross-connection of protection paths can be even more important than just increasing restoration-speed: when optical protection paths are pre-cross-connected, they can be guaranteed in advance to work when required. FIPP p-cycles therefore offer a fully pre-connected, alternative to SBPP in which protection paths must be assembled on the fly from spare wavelength channels. Design results from small networks in [A. Kodian, W. D. Grover (2005)] showed that FIPP p-cycle designs can be as efficient as SBPP but it is very difficult to design larger networks using the ILP design model in [A. Kodian, W. D. Grover (2005)]. We now develop a new ILP model and a related heuristic method for FIPP p-cycle design that produces network designs with much faster runtimes. Results indicate that the heuristic generates FIPP p-cycle designs that have total capacity costs within 10-18% of optimally designed SBPP solutions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".