Three Case Studies of Large-Scale Data Flows

William Y. Arms, Selcuk Aya, Manuel Calimlim, Jim Cordes, J. Deneva, Pavel A. Dmitriev, J. Gehrke, L. Gibbons, C. D. Jones, V. Kuznetsov, D. Lifka, Mirek Riedewald, D. Riley, A. Ryd, G. Sharp
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引用次数: 7

Abstract

We survey three examples of large-scale scientific workflows that we are working with at Cornell: the Arecibo sky survey, the CLEO high-energy particle physics experiment, and the Web Lab project for enabling social science studies of the Internet. All three projects face the same general challenges: massive amounts of raw data, expensive processing steps, and the requirement to make raw data or data products available to users nation- or world-wide. However, there are several differences that prevent a one-sizefits- all approach to handling their data flows. Instead, current implementations are heavily tuned by domain and data management experts. We describe the three projects, and we outline research issues and opportunities to integrate Grid technology into these workflows.
大规模数据流的三个案例研究
我们调查了三个大规模科学工作流程的例子,我们正在康奈尔大学工作:阿雷西博天空调查,CLEO高能粒子物理实验,以及网络实验室项目,使互联网的社会科学研究成为可能。这三个项目都面临着同样的挑战:大量的原始数据、昂贵的处理步骤,以及向全国或全世界的用户提供原始数据或数据产品的需求。然而,有几个不同之处阻止了一刀切的方法来处理它们的数据流。相反,当前的实现是由领域和数据管理专家进行大量调优的。我们描述了这三个项目,并概述了将网格技术集成到这些工作流中的研究问题和机会。
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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