Dynamic Virtual Network Embedding Leveraging Neighborhood and Preceding Mappings Information
Bibliographic record
Abstract
Future transportation systems are primarily based on the concept of the Internet of Vehicles (IoV). However, to fully unleash the potential of IoV, Network Virtualization (NV) is regarded as one of the key enablers. With NV, heterogeneous service requests can be deployed quickly, cost-effectively, and on-demand on a shareable infrastructure to meet stringent resource requirements. Virtual Network Embedding (VNE), one of the main challenges in NV, has been extensively investigated in the data-center paradigm, in which the network topology is static. Some existing VNE solutions have tackled the VNE problem in data-center networks while considering IoV demands, but very few have directly solved the problem considering vehicle mobility. Therefore, solving the online VNE problem in dynamic IoV environments, where connected and moving vehicles function as physical nodes to handle network service requests, still remains at an early stage. Towards that end, this paper proposes a novel heuristic algorithm that efficiently ranks available moving vehicles based on multiple network attributes, their neighborhood information, and the correlation of preceding mappings to tackle the online VNE problem in IoV. Moreover, we investigated the performance of several VNE algorithms using the Random Waypoint mobility model on different sizes of the Substrate Network (SN). We also introduce additional performance metrics to demonstrate the impact of vehicle mobility. Extensive simulation results indicate that the proposed algorithm performs better than state-of-the-art VNE algorithms in multiple performance metrics.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".