Fair Connectivity-Oriented Allocation for Combined Resources in VCC Networks
Bibliographic record
Abstract
The allocation and management of vehicular resources are essential in enabling services in Vehicular Cloud networks. Combined Resource Units (CRUs) allow for relaxed resource management by utilizing vehicular resources clustered in virtualized units and easing the fulfillment of service requests. Previous works have used mobility-based models such as SMDP and MDP for resource allocation. However, these models have presented significant system overhead, which has impacted the network's performance. Therefore, this work proposes a game theory model for assigning CRUs to satisfy service requests. The utility function of CRUs is maximized by playing a non-cooperative game between service requests. Two different game models are implemented based on exhaustive search and pruning methods. These models use distinct utility functions, which differ in terms of distance and signal strength of the CRUs. Comparing the performance of the two models, the pruning model offers a 90% success rate towards satisfying service requests.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".