Hierarchical Approach for Efficient Virtual Network Embedding Based on Exact Subgraph Matching
Bibliographic record
Abstract
The virtual network (VN) embedding problem is concerned with mapping the nodes and links of a VN request to a shared substrate network while maximizing some objective function such as maximizing profit or resource utilization. This mapping must satisfy specific node capacity and link bandwidth requirements. This paper presents a novel hierarchical approach for scalable VN embedding that achieves a balance between centralized schemes that have a network-wide view of available resources but represent a management bottleneck and scalable distributed ones that incur a high message overhead. The contribution of this work is two fold; we introduce a novel hierarchical substrate management framework that finds more than one candidate VN mapping in parallel, thus, increasing the chances of finding an optimal mapping. The second contribution is a novel VN mapping scheme that recasts the VN mapping problem as a subgraph matching one using modified graph-powers, and introduces a simple heuristic matching scheme to find an efficient VN mapping. In contrast to existing solutions, the proposed framework does not impose any limitations on the size or topology of the VN request, rather the search is tailored based on the VN size. Experimental results demonstrate the performance of the proposed scheme.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".