Spare capacity planning using survivable alternate routing for long-haul WDM networks
Bibliographic record
Abstract
Spare capacity planning is designed to find the minimum amount of spare capacity to be allocated throughout a network so that the network can survive from network component failures. In this paper, the spare capacity planning problem is investigated for long-haul wavelength division multiplexing (WDM) networks. A three-step method is developed for solving the problem. First, heuristic approaches are used to select both candidate working routes and protection routes in order to achieve approximate optimal performance while maintaining a computational feasibility. Second, traffic requests are distributed on the obtained candidate working and protection routes optimally using genetic algorithms (GA). Finally wavelengths are assigned to working lightpaths and shared protection lightpaths. The major advantage of the new approach is the ability to incorporate nonlinear constraints and nonlinear cost functions into the GA, which are introduced by sharing protection links between shared risk link groups (SRLG). Moreover, by considering SRLG constraints in the spare capacity planning phase, wavelengths can be allocated to each shared protection route before failures happen, so that shorter restoration latency can be achieved. Numerical results illustrate that the proposed approach is more cost-effective than the single-path protection method.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".