p-Cycle based dual failure recovery in WDM mesh networks
Bibliographic record
Abstract
We propose a new optimization solution method for the design of dual failure survivable p-cycle based WDM mesh networks that guarantee a quantified service availability under different dual failure probability distributions. Nowadays, network providers are facing the challenge of meeting the specifications of service-level agreements (SLAs) with their corporative customers. Therefore, it is of interest to understand and quantify the service availability in order to allow a comparison of the delivered qualities of services with the guaranteed ones, and thus to offer safe SLAs and competitive services. We therefore propose to investigate further the relationship between network physical topologies and the required amount of spare capacity to attain an optimized dual failure recovery level. In order to properly address the scalability issue related to the offline enumeration of the candidate p-cycles, we develop a solution method based on column generation techniques where a very limited number of valued p-cycles are dynamically generated during the optimization process. Depending on the network connectivity, the results show that a spare capacity investment of 2.3 to 5.2 times the amount of protected capacity is necessary, in order to guarantee a 100% dual failure restorability, when using p-cycles in survivable mesh networks.
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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 | 0.001 |
| 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 | 0.001 |
| 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".