On optimization of scientific workflows to support streaming applications in distributed network environments

Qi-Shi Wu, Yi Gu, Xukang Lu, Mengxia Zhu, P. Brown, Wuyin Lin, Yangang Liu
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引用次数: 3

Abstract

Large-scale data-intensive streaming applications in various science fields feature complex DAG-structured workflows comprised of distributed computing modules with intricate inter-module dependencies. Supporting such workflows in high-performance network environments and optimizing their throughput are crucial to collaborative scientific exploration and discovery. We formulate workflow mapping as a frame rate optimization problem and propose an efficient heuristic solution, which is integrated into the Condor-based Scientific Workflow Automation and Management Platform (SWAMP) in place of Condor's default mapping scheme. The SWAMP system is also augmented with several new components to improve the workflow management process. The performance superiority of the proposed solution is verified using both simulations and a real-life scientific workflow for climate modeling deployed in a distributed heterogeneous network environment.
分布式网络环境下支持流应用的科学工作流优化研究
各种科学领域的大规模数据密集型流应用具有复杂的dag结构工作流,该工作流由具有复杂模块间依赖关系的分布式计算模块组成。在高性能网络环境中支持此类工作流并优化其吞吐量对于协作式科学探索和发现至关重要。我们将工作流映射作为一个帧率优化问题,提出了一种高效的启发式解决方案,并将其集成到基于Condor的科学工作流自动化与管理平台(SWAMP)中,取代了Condor的默认映射方案。SWAMP系统还增加了几个新组件,以改进工作流管理过程。通过模拟和部署在分布式异构网络环境中的气候建模的真实科学工作流程,验证了所提出的解决方案的性能优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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