H3DRA: Achieving resilient task scheduling for zero-interruption vehicular edge computing in sixth-generation networks

Author Identifier (ORCID)

Wei Ni: https://orcid.org/0000-0002-4933-594X

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 (H3DRA) 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 H3 DRA outperforms classical algorithms by 48% in stability and by 32% in task throughput.

Keywords

Hierarchical reinforcement learning, task scheduling, vehicular edge computing, zero interruption

Document Type

Journal Article

Date of Publication

1-1-2026

Volume

75

Issue

3

Publication Title

IEEE Transactions on Vehicular Technology

Publisher

IEEE

School

School of Engineering

Comments

Zhang, J., Zou, S., Liwang, M., Sun, Y., Ni, W., & Wang, X. (2026). H3DRA: Achieving resilient task scheduling for zero-interruption vehicular edge computing in sixth-generation networks. IEEE Transactions on Vehicular Technology, 75(3), 4797-4809. https://doi.org/10.1109/TVT.2025.3609780

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First Page

4797

Last Page

4809

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Link to publisher version (DOI)

10.1109/TVT.2025.3609780