Efficient and scalable design of Protected Working Capacity Envelope
Bibliographic record
Abstract
The Protected Working Capacity Envelope (PWCE) concept was proposed by Grover (2004) in order to simplify network and operation management in survivable WDM networks. In this paper, we focus on PCWE with p-cycles and investigate a new design method, highly efficient and scalable, for designing survivable WDM networks. Traditional design methods proceed in two steps: A first step where a large (sometimes huge) number of cycles is enumerated followed by a second step where the selection of the most promising p-cycles is made with the help of combinatorial optimization tools. We develop a new (single step) method based on large scale optimization tools, i.e., column generation techniques, where the generation of cycles is dynamic and embedded within the optimization process. The key advantage of column generation (CG) techniques is that no a priori cycle enumeration step is required ahead of the optimization process: The generation of the relevant cycles, only one or few at a time, is embedded in the optimization process. We conducted intensive computational experiments. Not only do we considered several network instances with quite different topology characteristics, but we also compared our CG-based model and solution method with several existing models and methods from the literature. Results obtained in the experiments on five different network instances, show that the CG-based model and method outperform by far the results of all previous studies, both with respect to the scalability (much smaller computing times for large network instances) but also with respect to the quality of the 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 | 0.000 |
| 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) | 0.002 | 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".