多代理深度强化学习用于联合任务卸载和资源分配在IIoT中,具有动态优先级
Yongze Ma1,2, Yanqing Zhao1,2, Yi Hu1,2
1Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
本研究引入了一个动态优先意识方法,用于边缘计算中的任务调度. 它优化了任务卸载和资源分配,提高了工业物联网 (IIoT) 系统的效率.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 分布式系统 分布式系统
背景情况:
- 工业物联网 (IIoT) 终端的扩散增加了边缘计算中的任务并发性和资源需求.
- 现有的任务安排方法在竞争优势环境中难以平衡卸载,资源分配和优先级适应.
- 云端端协作计算提供了潜力,但需要共同优化以提高效率.
研究的目的:
- 为边缘计算提出一种新的两阶段动态优先意识的联合任务卸载和资源分配方法 (DPTORA).
- 在动态优先级和资源限制下,解决平衡任务执行和资源利用现有方法的局限性.
- 在复杂的IIoT环境中提高任务安排的效率和性能.
主要方法:
- 开发了两阶段的方法:第一阶段使用了改进的多代理近接政策优化 (MAPPO) 与优先级注意模块 (PGAM) 进行动态优先级意识下载.
- 第2阶段将资源分配表达为一个单一目标的凸优化问题,通过拉格朗的双重方法来解决.
- 集成的云端端协作计算原则,用于跨层任务卸载.
主要成果:
- 拟议的DPTORA方法显著降低了任务延迟和能源消耗.
- 与现有的多代理强化学习基线相比,DPTORA的任务完成率更高.
- 在动态优先级下,MAPPO-PGAM集成提高了下载策略的稳定性和准确性.
结论:
- 在资源有限的边缘计算场景中,DPTORA有效地平衡了任务执行和资源利用.
- 任务卸载,资源分配和优先级调整的联合优化对于高效的IIoT任务安排至关重要.
- 提出的方法为边缘计算环境中的智能任务调度提供了显著的进步.
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