Joint Node-Link Algorithm for Embedding Virtual Networks with Conciliation Strategy
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Bibliographic record
Abstract
Network virtualization (NV) has widely envisioned as a crucial factor for the success of the future networks by enabling a flexible, cost-effective and on-demand deployments of multiple network service requests on a shared physical infrastructure. The major challenge of NV is to efficiently and effectively embed heterogeneous virtual network requests (VNRs), consisting of a set of virtual nodes connected by virtual links, onto a shared substrate network meeting various stringent resource constraints. Most of the research papers in this field have merely focused on separate virtual node mapping (VNoM) or virtual link mapping (VLiM) with scalable heuristic algorithms. The lack of a coordination between node and link mapping stages results in low acceptance ratio as well as network revenues. In this paper, we propose a new approach based on Genetic Algorithm (GA) that jointly coordinates node and link mappings where the link mapping is relied on a path ranking method. A novel heuristic conciliation mechanism is introduced to handle a possible set of infeasible link mappings during gener-ating virtual embedding solutions in GA's operations. Extensive evaluation results show that our proposed GA-based algorithm outperforms state-of-the-art virtual embedding algorithms in all performance metrics we adopted.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 it