A Virtual Node-based Shared Restoration scheme in multi-domain networks
Bibliographic record
Abstract
Existing restoration schemes require detailed link-state information to be advertised between the nodes in a given network. These schemes become less attractive to networks with multiple autonomous domains where network link-state information needs to be abstracted within each domain for efficiency and scalability reasons. In this paper, we present a distributed end-to-end shared restoration scheme, referred to as Virtual Node-based Shared Restoration (VNSR), which provides routing and shared restoration across multiple domains with limited information exchange among the domains. With this scheme, every domain is modeled as a single virtual node with a certain internal capacity that can be advertised to other domains. This minimum advertised information is used to compute a pair of link-disjointed paths between any given source and destination nodes across the domains. The performance of the proposed scheme is evaluated and compared with another published scheme, which modeled every domain as a set of virtual paths. We will show that the VNSR scheme is more scalable and efficient in terms of the routing overhead, while still yielding the same capacity performance, compared with the published scheme.
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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 | 0.000 |
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 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".