Design of flexible protection plans in survivable WDM networks: An application to PWCE
Bibliographic record
Abstract
We propose a new flexible design approach of protection plans in survivable WDM networks by using protection structures with no predefined shapes in order to maximize the protected working capacity in an end-to- end basis. Previous design approaches of survivable WDM based on Protected Working Capacity Envelope (PWCE) have looked at the optimization problem of maximizing the protected capacity on a link basis, independently of the source and destination nodes of the potential traffic. Moreover, those approaches have only investigated the design problem with pre-configured protection cycles (p- cycles). Our design approach proposed in this paper differs from those previously proposed in two main points: (i) We use pre-configured protection structures (p-structures) with no predefined shapes. By using protection structures with unrestricted shapes, we want to identify the most flexible ones, i.e., those that can provide the highest protected capacity even within constrained spare capacity budget or low network connectivity. (ii) We maximize the availability of the protected capacity on an end-to-end basis rather than on a link basis. This allow us to more efficiently track fluctuation of the traffic in the networks and among nodes. In order to deal with the large solution space, we develop an ILP optimization model, and use an efficient large scale optimization tool called the Column Generation tool (CG). Results show that a design based on unrestricted p-structure patterns is ~ 10% less capacity redundant, ~ 15% more reliable, and allow recovery along shorter backup paths compared to the p-cycle based scheme.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".