Cycle-oriented distributed preconfiguration: ring-like speed with mesh-like capacity for self-planning network restoration
Bibliographic record
Abstract
Cycle-oriented preconfiguration of spare capacity is a new idea for the design and operation of mesh-restorable networks. It offers a sought-after goal: to retain the capacity-efficiency of a mesh-restorable network, while approaching the speed of line-switched self-healing rings. We show that through a strategy of pre-failure cross-connection between the spare links of a mesh network, it is possible to achieve 100% restoration with little, if any, additional spare capacity than in a mesh network. In addition, we find that this strategy requires the operation of only two cross-connections per restoration path. Although spares are connected into cycles, the method is different than self-healing rings because each preconfigured cycle contributes to the restoration of more failure scenarios than can a ring. Additionally, two restoration paths may be obtained from each pre-formed cycle, whereas a ring only yields one restoration path for each failure it addresses. We give an optimal design formulation and results for preconfiguration of spare capacity and describe a distributed self-organizing protocol through which a network can continually approximate the optimal preconfiguration state.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".