A new flow formulation for FIPP p-cycle protection subject to multiple link failures
Bibliographic record
Abstract
We propose a new generic flow formulation for Failure-Independent Path-Protecting (FIPP) p-cycles subject to multiple failures. While our new model resembles the decomposition model formulation proposed by Orlowski and Pioro (2011) in the case of classical shared path protection, its originality lies in its adaptation to FIPP p-cycles. When adapted to that last pre-configured pre-cross connected protection scheme, the bandwidth sharing constraints must be handled in a different way in order to take care of the sharing along the FIPP p-cycles. It follows that, instead of a polynomial-time solvable pricing problem as in the model of Orlowski and Pioro (2011), we end up with a more complex pricing problem, which is no more polynomially solvable. We therefore focused on speeding up the iterative solution process of the pricing problems using a hierarchical decomposition of the original pricing problem. Performance evaluation is made in the case of FIPP p-cycles subject to dual failures. The proposed model remains fairly scalable for increasing percentages of dual failures, and requires much less bandwidth than p-cycle protection schemes (ratio varies from 2 to 4). In the particular case of single link failures, it compares favorably to the previously proposed column generation ILP model of Rocha, Jaumard and Stidsen (2011).
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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 | 0.002 | 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".