IntelliScheduler:一个边缘云计算环境混合深度学习框架,用于基于学习的任务调度
L Raghavendar Raju1, M Venkata Krishna Reddy2, Sridhar Reddy Surukanti3
1Dept. of Computer Science and Engineering, Matrusri Engineering College, Hyderabad, India. lraghavendarraju@matrusri.edu.in.
Scientific reports
|February 27, 2026
概括
在边缘云系统中,IntelliScheduler使用深度强化学习来进行适应性任务调度. 这种方法显著减少了任务执行的延迟,并改善了物联网 (IoT) 应用中的体验质量 (QoE).
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:
- 边缘云计算对于需要低延迟的物联网 (IoT) 应用程序至关重要.
- 异质的截止日期和动态的工作负载挑战了服务水平协议 (SLA) 合规性和服务质量 (QoS).
- 现有的以云为中心和启发式调度方法缺乏适应不断变化的条件的适应性,导致延迟.
研究的目的:
- 为边缘云系统开发一个适应性任务调度框架.
- 尽量减少总任务执行延迟,提高资源利用率.
- 增强服务水平协议 (SLA) 合规性和体验质量 (QoE).
主要方法:
- 介绍了IntelliScheduler,这是一个混合的演员-关键的深度强化学习框架.
- 开发了一个运行时意识到的状态表示和基于学习的决策机制.
- 实现了基于学习的最佳任务调度 (LbOTS) 算法,并采用了延迟意识奖励建模.
主要成果:
- 与基线方法相比,达到高达13%的正常化奖励和15-75%更好的QOE.
- 报告说,培训损失降低了67%,运营成本降低了52-66%,拒绝率降低了80-90%.
- 在自适应任务调度效率方面显著改进.
结论:
- 拟议的IntelliScheduler框架有效地解决了边缘云环境中的自适应任务调度挑战.
- LbOTS算法优化了任务部署,以减少延迟和提高性能.
- 适应式学习的配方在动态边缘云调度场景中具有很高的相关性.
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