Optimizing Service Allocation in Vehicular Cloud Networks: A User-Preference-Based Approach using NSGA-II
Bibliographic record
Abstract
Vehicular Cloud Networks (VCNs) represent a promising paradigm for delivering dynamic, low-latency services by leveraging the resources of connected vehicles. However, the highly mobile and resource-constrained nature of VCNs poses significant challenges for reliable service allocation, particularly when considering user-specific preferences. In this paper, we propose a user-preference-based service allocation mechanism that optimizes three key Service Level Agreement (SLA) parameters: delay, availability, and price. Our approach utilizes the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to generate Pareto-optimal solutions, allowing users to select services that best match their preferences. We evaluate the proposed approach through simulation, comparing its performance against alternative methods in terms of execution time, SLA adherence rate, and service stability. The results demonstrate that our approach not only achieves higher SLA adherence but also maintains efficiency under various vehicular conditions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".