On Monitoring and Failure Localization in Mesh All-Optical Networks
Bibliographic record
Abstract
Achieving fast and precise failure localization has long been a highly desired feature in all-optical mesh networks. M-trail (monitoring trail) has been proposed as the most general monitoring structure for achieving unambiguous failure localization (UFL) of any single link failure while effectively reducing the amount of alarm signals flooded in the networks. However, it is critical to come up with a fast and intelligent m-trail design approach for minimizing the number of m-trails and the totally consumed bandwidth, which ubiquitously determines the length of alarm code and bandwidth overhead for the M-trail deployment, respectively. In this paper, the m-trail design problem is investigated. To gain deeper understanding of the problem, we firstly conduct a bound analysis on the minimum length of alarm code required for UFL. Then, a novel algorithm based on random code assignment (RCA) and random code swapping (RCS) is developed for solving the m-trail design problem. The algorithm prototype can be found in. The algorithm is verified by comparing with an integer linear program (ILP), and the results demonstrate its superiority in minimizing the fault management cost and bandwidth consumption while achieving significant reduction in computation time. To investigate the impact of topology diversity, extensive simulation is conducted on thousands of random network topologies with systematically increased network connectivity. Lastly, we provide abundant discussions and interesting conclusive remarks that position our discoveries.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".