Intrinsically Resilient Optical Backbones: An Efficient Ring-Based Interconnection Paradigm
Bibliographic record
Abstract
Physical topologies are evolving from elementary survivable rings into complex mesh networks. Nevertheless, no topology model is known to provide an economic, systematic, and flexible interconnection paradigm for ensuring that those meshes bear resilience features. This paper argues that intrinsic resilience can be brought by twin graph topologies, as they satisfy equal length disjoint path property with minimal number of physical links. An exhaustive investigation is performed across twin graph families composing networks from 4 to 17 nodes, whereas diverse real-world topologies and ring networks are used as benchmarks. First, we illustrate the growing process, and discuss the topology diversity of twin graphs. We analyze the impact of single cable cuts between neighbouring nodes, then we stress topologies with 2, 3, and 4 simultaneous cable cuts. Improved resiliency is seen for neighbor nodes and also reduction of cut sets able to disconnect the twin topologies in comparison with real-world networks. Finally, we present as a use case the redesign of CESNET into a resilient network.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".