Bibliographic record
Abstract
Our focus, in this paper is on the embedding problem which consists on the mapping of Virtual Network (VN) resources onto physical infrastructure network. In relevant literature, number of works have been proposed to solve this challenging problem. However, few proposals have been dedicated to provide backup mapping mechanism for failing physical links. In virtualization environment a failing link will affect all VNs that span over this resource. To address these concerns, we propose in this work a resilient VN embedding (RVNE) approach that: (a) uses an adaptation of a Column Generation-based technique developed in our previous work, to calculate in a first stage a cost-efficient VN mapping while minimizing the affects of a single link failure in VN layer, and (b) proposes in a second stage a p-Cycle based VN protection approach that minimizes the backup resources while providing a full protection scheme. Experiments on large mix of VN requests show a clear advantage of resilient VN embedding model over benchmarks in terms of VN revenue, VN protection cost and resources utilization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".