BReW: Blackbox resource selection for e-Science workflows
workflows in support of large-scale science(2013)
摘要
Workflows are commonly used to model data intensive scientific analysis.
As computational resource needs increase for eScience, emerging platforms
like clouds present additional resource choices for scientists and
policy makers. We introduce BReW, a tool enables users to make rapid,
highlevel platform selection for their workflows using limited workflow
knowledge. This helps make informed decisions on whether to port
a workflow to a new platform. Our analysis of synthetic and real
eScience workflows shows that using just total runtime length, maximum
task fanout, and total data used and produced by the workflow, BReW
can provide platform predictions comparable to whitebox models with
detailed workflow knowledge.
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关键词
cloud computing,data analysis,natural sciences computing,workflow management software,BReW,blackbox resource selection,clouds,data intensive scientific analysis,e-science workflows,HPC,cloud,planning,resource platforms,resource selection,workflow,workflow migration
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