EPRS:在边缘集群中的经验优先强化调度器
Shuya Tan1, Tiancong Huang1, Enguo Zhu2
1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
一个新的基于容器的调度框架与经验优先强化调度器 (EPRS) 增强了边缘计算中的负载平衡. 这种方法可以在动态边缘环境中提高资源利用率和性能.
科学领域:
- 分布式系统 分布式系统
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 边缘计算环境是动态和异质的,这给任务安排带来了挑战.
- 传统的调度算法与负载波动作斗争,导致失衡和不足利用.
- 有效的负载均衡对于优化边缘集群中的资源利用至关重要.
研究的目的:
- 为增强负载平衡提出基于容器的边缘集群调度框架.
- 引入经验优先强化调度器 (EPRS),用于适应性任务调度.
- 改进异质边缘环境中的多维资源配置.
主要方法:
- 开发了一个基于容器的调度框架,具有实时资源监控器.
- 实施了经验优先强化计划器 (EPRS),使用优先驱动的样本选择.
- 通过考虑基于Kubernetes集群中的节点指标和任务要求来优化资源配置.
主要成果:
- 拟议的框架显著改善了多维负载平衡性能.
- 与现有的基于强化学习的调度器相比,平均获得了28.25%的收益.
- 与传统的调度算法相比,显示了29.78%的改进.
结论:
- 与EPRS集成的框架有效地解决了边缘计算中的负载平衡挑战.
- 该方法在动态边缘环境中提高了资源利用和调度效率.
- 通过在基于Kubernetes的边缘集群上的实现和实验来验证.
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