相关实验视频
基于深度强化学习学习的云端资源调度和卸载优化
Lili Yin1, Yunze Xie1, Ze Zhao1
1School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
本研究引入了用于智能制造的深度强化学习算法,显著减少工业物联网 (IoT) 环境中的任务中断和延迟. 该方法有效地管理动态边缘节点负载,用于实时处理.
科学领域:
- 智能制造 智能制造是一种智能制造.
- 物联网 (IoT) 的工业互联网.
- 边缘计算 边缘计算
背景情况:
- 智能制造依赖于工业物联网 (IoT) 设备,产生许多需要实时处理的延迟敏感任务.
- 边缘节点负载的动态变化导致延迟增加和任务中断,这给云端边缘端协作带来了挑战.
- 现有的任务卸载策略与未知的边缘节点负载和动态系统状态作斗争.
研究的目的:
- 提出一种分布式算法,用于在智能制造环境中有效卸载任务.
- 为了应对未知的边缘节点负载和动态系统状态变化的挑战.
- 优化对延迟敏感任务的任务分配和执行顺序.
主要方法:
- 基于深度强化学习的分布式算法,结合了卷积神经网络 (CNN) 和Informer架构.
- CNN提取边缘节点负载的局部特征;Informer的自我注意力捕捉了长期负载趋势.
- 集成决斗深度Q网络 (DQN) 和双DQN,用于精确的状态动作值函数近似.
主要成果:
- 拟议的算法可以将任务中断率降低82.3-94%.
- 与现有算法相比,平均延迟时间减少了28-39.2%.
- 该方法在高负载,延迟敏感的制造场景中显示出显著的优势.
结论:
- 开发的深度强化学习算法有效地处理动态边缘节点负载和系统不确定性.
- 移动设备的独立任务卸载决策使动态任务分配和优化执行成为可能.
- 该算法提供了一个强大的解决方案,用于实时处理与工业物联网的智能制造.
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