H$^{3}$DRA: Achieving Resilient Task Scheduling for Zero-Interruption Vehicular Edge Computing in Sixth-Generation Networks
Bibliographic record
Abstract
The paradigm shift toward 6G-enabled immersive services, including extended reality (XR) communication, holographic interaction, and distributed multisensor intelligence, imposes unprecedented challenges to vehicular networks. These mission-critical applications require zero-interruption continuity, deterministic ultra-low latency, and sustained high-throughput task execution in highly dynamic vehicular environments. To achieve stability, adaptability, and efficiency in 6G-vehicular edge computing (VEC), this paper proposes a new three-layer hierarchical deep reinforcement learning scheduling (H$^{3}$DRA) framework, which involves an interruption prediction-empowered adaptive task allocation (IPred-ATA) technique that dynamically optimizes scheduling destinations through proactive failure prediction and multi-objective utility maximization. We further design a clustering-assisted hierarchical reinforcement learning (CAHRL) algorithm that addresses the challenges of multi-hop path selection within dynamic network topologies by decomposing the large-scale, complex state-action space into manageable hierarchical structures. Simulations demonstrate that the proposed H$^{3}$DRA outperforms classical algorithms by 48% in stability and by 32% in task throughput.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".