On the efficacy of GMPLS auto-reprovisioning as a mesh-network restoration mechanism
Bibliographic record
Abstract
GMPLS provides standardized protocols through which nodes can request and establish (or release) lightpaths on demand between themselves and peer nodes. The primary intent is to support automated provisioning for dynamic demand environments. However, an apparently tempting assumption is that GMPLS also provides a mechanism for physical layer network restoration, wherein all effected node pairs "simply redial their connections" - simultaneously. We argue from basic considerations, and illustrate with experimental results, that this is an oversimplified view. It assumes that the problem of replacing a failed path is the same when the path fails in isolation and when numerous paths fail together from a cable cut. Without some form of preplanning, or overall coordination of the multiple simultaneous reprovisioning attempts in the latter case, no guarantees are possible about the overall extent or pattern of recovery level. Capacity over-provisioning can mitigate the risk, but may involve almost as much overprovisioning as would suffice for simple 1+1 signal duplication in the first place, which defeats one of the main aims (efficiency) of a mesh-oriented 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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".