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Proceedings of the second international workshop on Data-aware distributed computing最新文献

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Data Staging Strategies and Their Impact on the Execution of Scientific Workflows 数据分级策略及其对科学工作流执行的影响
S. Bharathi, A. Chervenak
Data intensive workflows process and generate large amounts of data. Strategies employed to stage data in and out of compute resources can often have a significant impact on the overall execution of a workflow. We study the relationships between data placement services that perform the staging and workflow managers that control the release of computational jobs. We describe a framework that classifies data staging strategies into decoupled, loosely-coupled and tightly-coupled modes, based on the degree of their interaction with the workflow manager. We present the results of simulation studies that investigate the effect of decoupled, loosely-coupled and tightly-coupled data staging strategies on synthetic workflows resembling those from real world scientific applications.
数据密集型工作流处理和生成大量数据。用于将数据导入和取出计算资源的策略通常会对工作流的整体执行产生重大影响。我们研究了执行分段的数据放置服务和控制计算作业释放的工作流管理器之间的关系。我们描述了一个框架,该框架根据数据分级策略与工作流管理器的交互程度,将数据分级策略分为解耦、松耦合和紧耦合模式。我们展示了模拟研究的结果,这些研究调查了解耦、松耦合和紧耦合数据分级策略对合成工作流的影响,这些工作流类似于现实世界的科学应用。
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引用次数: 18
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Proceedings of the second international workshop on Data-aware distributed computing
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