A Customized Genetic Algorithm for SLA-Aware Service Provisioning in Infrastructure-Less Vehicular Cloud Networks
Bibliographic record
Abstract
Vehicular Ad-hoc Networks (VANETs) and in-vehicle networks offer complementary perspectives on Intelligent Transportation Systems (ITS), enabling communication between vehicles and within individual vehicles, respectively. While VANETs focus on vehicle-to-vehicle communication, the growing demand for dynamic resource sharing and data processing across a fleet of vehicles highlights the need for Vehicular Cloud Networks (VCNs). VCNs, despite their lack of fixed infrastructure and the continuous mobility of vehicles, provide a promising solution for improving resource management and data sharing, making them critical for achieving efficient Service Level Agreements (SLAs) in infrastructure-less environments. This paper addresses these challenges by employing a hierarchical clustering technique and proposing a novel mathematical formulation for resource provisioning in infrastructure-less vehicular clouds. The formulation considers diverse criteria, including provider and requester mobility, data volume, and service delay tolerance, to ensure SLA adherence. A customized genetic algorithm is used to solve the maximization problem, incorporating a grouping mechanism for efficient problem solving. Simulations using the NS2 network simulator and the IBM CPLEX optimization tool validate the feasibility of the proposed approach and demonstrate its superior performance compared to the other methods.
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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 | 0.000 |
| 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) | 0.002 | 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".