Distributed parallel algorithms for online virtual network embedding applications
Bibliographic record
Abstract
Summary Network virtualization (NV) has ubiquitously emerged as an indispensable attribute to enable the success of the forthcoming virtualized networks (eg, 5G network and smart Internet of Things [IoT]). Virtual network embedding (VNE) is the major challenge in NV that allows multiple heterogeneous virtual networks (VNs) to simultaneously coexist on a shared substrate infrastructure. A great number of VNE algorithms have been proposed, but over the past decades, most of them are only targeting for VNE node mapping. In this paper, we propose two distributed parallel genetic algorithms, which are based on two versions of crossover and mutation schemes, for online VN link embedding problems with low latency and high efficiency. Furthermore, we conduct a time analysis on the executing time of independently distributed parallel computing machines in details. This comprehensive analysis validates the parallel computing scalability on an identical number of predefined parallel machines. Extensive simulations have shown that our proposed algorithms can achieve better performance than integer linear programming (ILP)–based solutions while meeting the stringent time requirements for online VN embedding applications. Our proposed algorithms yield superior performance in running time with 32.78% up to 1727.8% faster than existing popular VNE algorithms. Additionally, the theoretical analysis indicates that the execution time can be reduced to logarithmic times by applying proposed distributed parallel algorithms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".