A distributed hierarchial p-tree link protection scheme for mesh networks
Bibliographic record
Abstract
This paper presents a distributed scheme for link failure recovery in mesh optical networks, based on the use of network hierarchical spanning trees. The scheme intends to maximize restorability in a network with known working and spare capacities. The hierarchical protection tree (p-tree) provides the hierarchical layering of the network. The straddling links that are not located on the tree are protected through tree branches to the higher layer Parent nodes. The links on the tree are protected by links to backup parent nodes. We study the problem of finding the most optimized network tree to achieve maximum restorability, and present heuristics for finding the best tree. Our algorithm includes two steps: Selection of the best root node for the tree, and construction of the tree based on distribution of tree ID labels among the nodes. Each node selects a primary parent node and a backup parent node, and constructs pre-determined protection paths accordingly. In case of failure, all connections on a link are switched quickly to the protection path as a bundle. We perform restorability analysis for several real and arbitrary long-haul networks and show that our scheme provides excellent network restorability along with exceptional scalability and maintainability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".