多工作流应用的计划器导向调度策略

Zhifeng Yu, Weisong Shi
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引用次数: 121

摘要

近年来,由于集群环境的流行,工作流应用程序越来越受欢迎。自那以后,已经开发了许多算法,但是大多数静态算法都是在调度单个工作流应用程序的问题域设计的,因此不适用于多个工作流应用程序和其他独立作业竞争资源的常见集群环境。动态调度方法本质上可以处理混合工作负载,但由于没有工作流应用程序的全局视图,其性能还有待优化。最近的研究建议在执行之前将多个工作流合并为一个工作流,但是没有解决一个重要的问题,即不同的用户可能在不同的时间提交多个工作流应用程序。在本文中,我们提出了一种利用工作依赖信息和执行时间估计的多工作流应用计划器导向的动态调度策略。我们的方法动态调度单个作业,而不需要事先合并工作流应用程序。仿真结果表明,该算法在工作流makespan和周转时间方面分别比其他两种算法高出43.6%和36.7%,并且在并发工作流应用数量增加和资源稀缺时性能更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Planner-Guided Scheduling Strategy for Multiple Workflow Applications
Workflow applications are gaining popularity in recent years because of the prevalence of cluster environments. Many algorithms have been developed since, however most static algorithms are designed in the problem domain of scheduling single workflow applications, thus not applicable to a common cluster environment where multiple workflow applications and other independent jobs compete for resources. Dynamic scheduling approaches can handle the mixed workload practically by nature but their performance has yet to optimize as they do not have a global view of workflow applications. Recent research efforts suggest merging multiple workflows into one workflow before execution, but fail to address an important issue that multiple workflow applications may be submitted at different times by different users. In this paper, we propose a planner-guided dynamic scheduling strategy for multiple workflow applications, leveraging job dependence information and execution time estimation.Our approach schedules individual jobs dynamically without requiring merging the workflow applications a priori. The simulation results show that the proposed algorithm significantly outperforms two other algorithms by 43.6% and 36.7% with respect to workflow makespan and turnaround time respectively, and it performs even better when the number of concurrent workflow applications increases and the resources are scarce.
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