在异质计算环境中用于精确任务调度的QLSA-MOEAD集成
Abla Saad1, Osama Abd El-Raouf2, Mohiy Hadhoud3
1Faculty of AI, Machine Intelligence Dept, Minufiya University, Shebeen El-Kom, Egypt. abla.saad@ci.menofia.edu.eg.
Scientific reports
|February 17, 2026
概括
本研究介绍了QLSA-MOEAD,这是一个用于异质计算中的高效工作流程调度的新型框架. 它优化了任务调度,以尽量减少完成时间和最大限度地利用资源,优于现有的方法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 高性能计算 高性能计算
背景情况:
- 异质计算 (CPU,GPU,FPGA) 由于硬件多样性和复杂的依赖关系,造成了调度挑战.
- 现有的方法与冲突的目标作斗争,例如最小化产量和最大化资源利用.
- 像CyberShake这样的现实世界工作流引入了额外的目标,例如能源消耗.
研究的目的:
- 为异质计算环境开发一个高效的任务调度框架.
- 解决工作流程安排中的多目标优化问题,包括制作时间,资源利用和能源消耗.
- 提出一种混合方法,将Q-learning,模拟 Annealing和MOEA/D结合起来,以提高调度性能.
主要方法:
- 制定工作流程调度作为一个多目标优化问题.
- 开发QLSA-MOEAD,这是一个混合框架,整合了Q-learning,模拟化和MOEA/D.
- 评估合成 (FFT,分子) 和现实世界 (CyberShake,组装) 工作流程的框架.
主要成果:
- QLSA-MOEAD表现出卓越的性能,在14/16个FFT/分子案例中实现最佳解决方案质量,并且在CyberShake上表现优于基线.
- 该框架在一个大型的 Montage 工作流中显示了可扩展性,实时的任务到达.
- 通过Q学习,可以快速做出决策,响应时间在0.80-1.70毫秒之间.
结论:
- QLSA-MOEAD是一个有效的解决方案,用于在异质系统中的静态和动态工作流程调度.
- 混合方法平衡勘探和开采,导致可靠和高质量的调度决策.
- 该框架的性能通过全面的评估和统计测试来验证.
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