集成策略与科学工作流管理的数据密集型应用

A. Chervenak, David E. Smith, Weiwei Chen, E. Deelman
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引用次数: 9

摘要

随着科学应用程序以不断增长的速度生成和使用数据,管理日益复杂的分析和数据移动的科学工作流系统将变得越来越重要。我们的工作目标是通过使用策略来改进进出计算资源的数据分段,从而提高科学工作流的整体性能。我们开发了一个Policy Service,它向工作流系统提供关于如何存放数据的建议,包括关于数据传输顺序和传输参数的建议。Policy Service根据其对正在进行的传输、最近的传输性能和当前用于数据暂存在的资源分配的了解提供此建议。本文描述了策略服务的体系结构及其与Pegasus工作流管理系统的集成。它采用了一系列策略进行数据暂放,并给出了一个策略的性能结果,该策略在源站点和目标站点之间贪婪地分配数据传输流。结果显示了数据密集型工作流的性能改进:增强了Montage天文学工作流以执行额外的大数据分段操作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating Policy with Scientific Workflow Management for Data-Intensive Applications
As scientific applications generate and consume data at ever-increasing rates, scientific workflow systems that manage the growing complexity of analyses and data movement will increase in importance. The goal of our work is to improve the overall performance of scientific workflows by using policy to improve data staging into and out of computational resources. We developed a Policy Service that gives advice to the workflow system about how to stage data, including advice on the order of data transfers and on transfer parameters. The Policy Service gives this advice based on its knowledge of ongoing transfers, recent transfer performance, and the current allocation of resources for data staging. The paper describes the architecture of the Policy Service and its integration with the Pegasus Workflow Management System. It employs a range of policies for data staging, and presents performance results for one policy that does a greedy allocation of data transfer streams between source and destination sites. The results show performance improvements for a data-intensive workflow: the Montage astronomy workflow augmented to perform additional large data staging operations.
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