Decomposition Approaches for Virtual Network Embedding With One-Shot Node and Link Mapping
Bibliographic record
Abstract
Network virtualization is a promising new resource management approach that allows customized virtual networks (VNs) to be multiplexed on a shared physical infrastructure. In this paper, our focus is on the embedding of VN resources onto this infrastructure. Since this problem is known to be NP-hard, embedding proposals in literature are heuristic-based approaches that restrict the problem space in different dimensions. Limitations of these proposals are: (1) as embedding of VN links and nodes is performed in two separate stages, it may ensue in a high blocking of VN requests and a less efficient usage of substrate resources; and (2) as pricing of embedding resources is based on linear functions, it triggers no competition among VN users in order to maximize infrastructure provider profits. These drawbacks motivate us to propose a mathematical model that makes use of large-scale optimization tools and proposes a Column Generation (CG) formulation of the problem, coupled with branch-and-bound technique or rounding-off heuristic. We also propose a periodical planning of embedding process where profitable VN requests are selected through an auction mechanism. In our experiments with different substrate network topologies and many different VN request patterns, we show a clear advantage of auction-based CG models over present benchmarks .
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".