Survivability Approaches Using p-Cycles in WDM Mesh Networks Under Static Traffic
Bibliographic record
Abstract
The major challenge in survivable mesh networks is the design of resource allocation algorithms that allocate network resources efficiently while at the same time are able to recover from a failure quickly. This issue is particularly more challenging in optical networks operating under wavelength continuity constraint, where the same wavelength must be assigned on all links in the selected path. This paper proposes two approaches to solve the survivable routing and wavelength assignment RWA problem under static traffic using p-cycles techniques. The first is a non-jointly approach, where the minimum backup capacity against any single span failure is set up first. Then the working lightpaths problem is solved by first generating the most likely candidate routes for each source and destination s-d pair. These candidate routes are then used to formulate the overall problem as an ILP problem. Alternatively, for a more optimum solution, the problem can be solved jointly, where the working routes and the backup p-cycles are jointly formulated as an ILP problem to minimize the total capacity required. Furthermore, only a subset of high merit cycles that are most likely able to protect the proposed working paths is used in the formulation. Reducing the number of candidate cycles in the final formulation plays a significant role in reducing the number of variables required to solve the problem. To reduce the number of candidate cycles in the formulation, a new metric called Route Sensitive Efficiency (RSE) - has been introduced to pre-select a reduced number of high merit cycle candidates. The RSE ranks each cycle based on the number of links of the primary candidate routes that it can protect. The two approaches were tested and their performances were compared.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".