Connectivity-aware virtual network embedding
Bibliographic record
Abstract
The problem of ensuring virtual network (VN) connectivity in presence of multiple link failures in the substrate network (SN) is not well investigated in Network Virtualization (NV) literature. We name this problem as Connectivity-aware Virtual Network Embedding (CoViNE). Solving CoViNE will enable a VN operator to perform failure recovery without depending on the SN provider, similar to the IP restoration mechanisms in IP-over-WDM networks. There are two steps in solving CoViNE: i) finding the virtual links that should be embedded disjointly, and ii) finding a substrate resource efficient embedding that ensures the virtual link disjointness constraint. We present two solutions to the CoViNE problem. The first solution uses a heuristic to compute the disjointness constraint, while an optimization model is used for VN embedding. The second solution, in contrast, uses heuristic for both the steps, and thus can solve larger instances of the problem. We compare our solutions with a cut set based approach that ensures VN connectivity for a single substrate link failure. Evaluation results show that our heuristics allocate ∼15% extra resources on average compared to the cut set based optimal solution, and executes two to three orders of magnitude faster on the same problem instances.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".