Design of multi-granularity directed segment p-cycles
Bibliographic record
Abstract
In today optical WDM networks, capacity expansion is performed by the addition of transport blades (e.g., transceivers) at end nodes of optical fibers. It allows the multiplexing of optical channels (wavelengths) with different granularities. In this paper, we propose a new ILP-based design approach of survivable WDM multi-granularity segment p-cycle where the objective is to minimize the cost of spare equipment at switching nodes in order to provide 100% single link failure protection. We use column generation (CG) optimization techniques, an efficient large scale optimization tool, in order to integrate, in an on-line fashion, the p-cycles generated during the optimization process. We, therefore, avoid a time and space costly prior enumeration of the candidate p-cycles. Extensive experiments are conducted to compare the proposed nodal equipment cost optimized design of segment p-cycles with the classical link spare capacity optimization approach. Numerical results show that the protection design corresponding to spare node equipment optimization is more effective than the one based on link spare capacity.
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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 | 0.000 |
| 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".