NF-MORL:一种神经模糊的多目标强化学习框架,用于在雾计算环境中任务调度
Xiaomo Yu1,2,3, Ling Tang4, Jie Mi2
1Guangxi Colleges and Universities Key laboratory of Intelligent Logistics Technology, Nanning Normal University, Nanning, 530001, Guangxi, China.
Scientific reports
|December 26, 2025
概括
本研究介绍了神经模糊多目标强化学习 (NF-MORL),用于在雾网络中高效地安排任务. NF-MORL显著改善了性能指标,如制造量,能源使用,成本和可靠性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 分布式计算 (Distributed Computing) 是一种分布式计算.
背景情况:
- 物联网 (IoT) 产生了大量的数据,由于延迟和集中,使传统的云计算受到压力.
- 雾计算提供了一个分散的解决方案,通过将计算更接近数据源.
- 在动态,异质的雾环境中,有效的任务安排仍然是一个重大挑战.
研究的目的:
- 开发一个创新的框架,用于在雾网络中的任务安排.
- 解决传统云计算和现有的雾计算方法的局限性.
- 为了提高效率,减少延迟,并在雾环境中提高可靠性.
主要方法:
- 引入了一个神经模糊的多目标强化学习 (NF-MORL) 框架.
- 集成的Takagi-Sugeno模糊逻辑用于不确定性处理和优先解释.
- 雇佣了一个多目标的演员-关键代理来学习平衡产品,能源,成本和可靠性.
主要成果:
- NF-MORL可以将makepan降低高达35%.
- 实现了大约30%的能源效率提升.
- 运营成本降低了高达40%.
- 增加了多达37%的故障容忍度.
结论:
- 与最先进的技术相比,NF-MORL表现出卓越的性能.
- 该框架能够有效地适应不同的工作量大小和动态条件.
- 将模糊逻辑与强化学习相结合,可以创建弹性和高效的雾调度器.
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