On Signaling-Free Failure Dependent Restoration in All-Optical Mesh Networks
Bibliographic record
Abstract
Failure dependent protection (FDP) is known to achieve optimal capacity efficiency among all types of protection, at the expense of longer recovery time and more complicated signaling overhead. This particularly hinders the usage of FDP in all-optical mesh networks. As a remedy, this paper investigates a new restoration framework that enables all-optical fault management and device configuration via state-of-the-art failure localization techniques, such as the FDP restoration process. It can be implemented without relying on any control plane signaling. With the proposed restoration framework, a novel spare capacity allocation problem is defined and is further analyzed on circulant topologies for any single link failure, aiming to gain a solid understanding of the problem. By allowing reuse of monitoring resources for restoration capacity, we are particularly interested in the monitoring resource hidden property, where less or even no monitoring resources are consumed as more working traffic is in place. To deal with general topologies, we introduce a novel heuristic approach to the proposed spare capacity allocation problem, which comprises a generic FDP survivable routing scheme followed by a novel monitoring resource allocation method. Extensive simulation is conducted to examine the proposed scheme and verify the proposed restoration framework.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".