Learning-Based Load-Aware Heterogeneous Vehicular Edge Computing
Why this work is in the frame
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Bibliographic record
Abstract
Vehicular edge computing is an emerging enabler to support vehicular-based computation-intensive tasks. By reason of the time-varying vehicular wireless environments and the stochastic task generation, the dynamically unbalanced task load distribution among resource-constrained edge infrastructures leads to the performance bottleneck and low efficiency of computation resource utilization. We employ an aerial relay station that can establish relay connections between vehicles and nearby heterogeneous edge infrastructures to relieve this situation. The computation offloading strategy design in the multivehicle multi-edge infrastructure scenario that is closely linked to system latency performance will be particularly complicated. To address this issue, a model-free multi-agent reinforcement learning is adopted, and we propose a practical constraint in the problem formulation. Simulation experiments show that the proposed strategy can guarantee load balancing among edge infrastructures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.078 | 0.161 |
| Research integrity | 0.000 | 0.002 |
| 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 it