Rethinking Virtual Link Mapping in Network Virtualization
Bibliographic record
Abstract
Virtual Network Embedding (VNE) that addresses the embedding problems of heterogeneous virtual networks onto a physical limited-capacity infrastructure efficiently is a major challenge in network virtualization (NV). VNE is computationally intractable when considering various constraints on nodes and links, and is also known as NP-hard even in offline embedding. Although the VNE problems have received attentions over recent decades with a vast number of VNE solutions, the majority of them only focus on VNE node mapping, whilst leaving the link mapping stage for the shortest path method or multicommodity flow (MCF) algorithm. We persuasively argue that node and link mappings equally play pivotal roles to approach an efficient VNE solution. In this paper, we reassess the role of link mapping stage in VNE problem, and then propose a novel intelligent VNE orchestration which effectively implements a distributed parallel model to reduce the operation time remarkably. Extensive evaluation results show that our proposed algorithm is not only faster than state-of-the-art VNE algorithms in speed, but also better in all performance metrics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".