VCG auction-based approach for efficient Virtual Network embedding
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, our focus is on the embedding problem which consists on the mapping of Virtual Network (VN) resources onto physical network. In literature, number of approaches have been proposed for embedding problem where the following limitations can be noticed: (i) mapping of VN links and nodes is performed on two separate stages, which may ensue in a high blocking of VN requests, and (ii) pricing of resources are based on linear functions, accordingly there is no competition among VN users resulting in reduced profit for the Physical Infrastructure Provider (PIP). To address these concerns, we propose deploying a periodical one-shot node and link embedding approach that increases the PIP profit's and VN users satisfaction ratio by allocating resources based on auction mechanism. Experiments on large mix of VN requests show a clear advantage of auctioning based models over benchmarks in terms of PIP profit's, VN users acceptance ratio and resources utilization.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it