A Dynamic Vehicle-Ranking Approach for Online Virtual Network Embedding in Internet of Vehicles
Bibliographic record
Abstract
Internet of Vehicles (IoV), a special domain of Internet of Things (IoT), has become an indispensable platform for the success of future intelligent transportation. Virtual Network Embedding (VNE), enabling flexible, cost-effective and on-demand deployments of multiple network service requests on a shared physical infrastructure, has become a technological breakthrough in IoV. Typical VNE problems have been well studied in the data center infrastructure where the physical topology is always static. Recently, researchers have investigated the VNE problem in data center networks while considering IoV demands. However, the VNE problem in IoV environments in which connected moving vehicles serve as substrate nodes to process service requests is still in its infancy. This paper proposes a novel heuristic algorithm for solving the online VNE problem in IoV by efficiently and rapidly ranking available vehicles based upon network attributes, and the knowledge of the preceding mappings. Extensive evaluation results indicate that the proposed solution not only outperforms several existing algorithms, but is also highly practical due to its fast execution time.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".