Monitoring Cycle Design for Fast Link Failure Localization in All-Optical Networks
Bibliographic record
Abstract
A monitoring cycle (m-cycle) is a preconfigured optical loop-back connection of supervisory wavelengths with a dedicated monitor. In an all-optical network (AON), if a link fails, the supervisory optical signals in a set of m-cycles covering this link will be disrupted. The link failure can be localized using the alarm code generated by the corresponding monitors. In this paper, we first formulate an optimal integer linear program (ILP) for m-cycle design. The objective is to minimize the monitoring cost which consists of the monitor cost and the bandwidth cost (i.e., supervisory wavelength-links). To reduce the ILP running time, a heuristic ILP is also formulated. To the best of our survey, this is the first effort in m-cycle design using ILP, and it leads to two contributions: 1) nonsimple m-cycles are considered; and 2) an efficient tradeoff is allowed between the monitor cost and the bandwidth cost. Numerical results show that our ILP-based approach outperforms the existing m-cycle design algorithms with a significant performance gain.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".