Distributed Parallel VN Embedding Based on Genetic Algorithm
Bibliographic record
Abstract
Network virtualization has emerged to replace traditional network architecture since it allows multiple virtual networks to share a common substrate network. However, one of the main challenges for network virtualization is the resource allocation for each virtual network (VN), called Virtual Network Embedding Problem. The computation complexity of existing resource allocation approaches is too high to achieve an optimum within an acceptable time. Further, the provided optimum is not optimal in an online non-reconfigurable VN embedding setting because of the highly dynamic nature of user demands. Nowadays, due to lower hardware costs, distributed parallel computing can be used to deal with complex computing tasks with high efficiency. In this paper, we propose a distributed parallel Genetic Algorithm (GA) for solving VN Embedding problems. Through theoretical analysis, we compare the time saving of our distributed parallel algorithm with traditional sequential running. Results show that our algorithm achieves better performances on execution time and acceptance ratio.
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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 | 0.001 | 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".