A global approach to fully pre-cross connected protection schemes design using p-structures
Bibliographic record
Abstract
We propose a general framework to optimize the design of fully pre-cross connected protection schemes in survivable transparent wavelength division multiplexing (WDM) networks using unrestricted shape protection structures (p-structures). Shared protection schemes that are fully pre-cross connected are based on predefined shape structures like p-cycles, p-trails, and FIPP p-cycles. These structures are characterized by their a priori efficiency (AE), i.e., a priori capacity efficiency and recovery delay. When deployed in networks where different traffic distributions and spare capacity budgets are assumed, their effective capacity efficiency usually becomes far inferior to their AE, which explains their lack of flexibility in provisioning protected capacity. In this paper, we propose design methods for link and path protection using all possible p-structures, provided that they can be fully pre-cross connected ahead of any failure. We use p-structures of a-priori known protection performance (capacity sharing and recovery delay), but of unknown shapes, and show that fully pre-cross connected protection structures can take a variety of shapes, different from cycles and trails. We propose an optimized design using a large scale optimization tool, namely Column Generation (CG). In our CG based approach, the shape of the p-structures is dynamically decided on during the optimization process, and adapted to the traffic distribution. The results show that, by combining all possible p-structures that can be fully pre-cross connected, about 15% of protection capacity can be saved in the design cost.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".