On the efficacy of GMPLS auto-reprovisioning as a mesh-network restoration mechanism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it