Joint N ode- Link Embedding Algorithm based on Genetic Algorithm in Virtualization Environment
Bibliographic record
Abstract
Virtual network embedding (VNE), that efficiently tackles the mapping problems of heterogeneous virtual networks onto a shared physical infrastructure meeting rigid resource constraints, is the major challenge in network virtualization (NV). VNE is widely known as NP-hard due to its intractable computation. The majority of VNE solutions have concentrated upon virtual node mapping (VNoM) and virtual link mapping (VLiM) separately. Uncoordinated approaches would facilitate algorithmic implementation, but they lead to low acceptance ratio, network revenues and high embedding cost. In this paper, we propose a new approach relied on Genetic Algorithm (GA), that coordinately joints node and link mappings where the link embedding is based on a fast and efficient sequential path searching method. A novel heuristic conciliation algorithm is presented to deal with a set of infeasible link mappings during producing VNE solutions in GA's operations. Extensive evaluation results indicate that our proposed approach outperforms state-of-the-art VNE algorithms in all adopted performance metrics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".