Survey and performance comparison of dynamic provisioning methods for optical shared backup path protection
Bibliographic record
Abstract
Considerable current research involves comparison of different schemes for dynamic service provisioning to the established method of shared backup path protection (SBPP). But there are many possible approaches to SBPP implementation, so often it is not clear what the "best" algorithm is to use for an SBPP reference solution. Having found this problem in our own ongoing studies, we decided to conduct an up to date survey and study on various shared backup path protection (SBPP)-based survivable lightpath service provisioning methods. Methods are compared from the aspects of the operational complexity and blocking performance. The tradeoff between more detailed routing information and efficiency of protection capacity use is portrayed over the range of algorithms. For networks with full routing information, we find that compared to the well-known two-step process, an iterative route searching process can greatly improve the network blocking performance in a network with sparse topology. The "trap topology" underlies this effect. The study also finds that there are strategies of searching for working paths that improve the blocking performance relative compared searches structured on hop length. This paper is a shortened version of our survey study on SBPP service provisioning methods and considers only optical networks with full wavelength conversion capability.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".