Integrating prediction, provenance, and optimization into high energy workflows
Journal of Physics: Conference Series(2017)
摘要
We propose a novel approach for efficient execution of workflows on distributed resources. The key components of this framework include: performance modeling to quantitatively predict workflow component behavior; optimization-based scheduling such as choosing an optimal subset of resources to meet demand and assignment of tasks to resources; distributed I/O optimizations such as prefetching; and provenance methods for collecting performance data. In preliminary results, these techniques improve throughput on a small Belle II workflow by 20%.
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关键词
high energy workflows,provenance,high energy
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