Scalable Workflow-Driven Hydrologic Analysis in HydroFrame
Scalable Workflow-Driven Hydrologic Analysis in HydroFrame
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DOI：
10.1007/978-3-030-50371-0_20
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发表时间：
2020-05-26
期刊：
影响因子：
--
通讯作者：
Altintas I
中科院分区：
文献类型：
--
作者：
Purawat S;Olschanowsky C;Condon LE;Maxwell R;Altintas I
HydroFrame项目是一个社区平台，旨在促进美国各地的综合水文建模。作为HydroFrame的一部分，我们寻求设计创新的工作流程解决方案，为三个目标用户群体创建水文分析的途径：建模师，分析师和领域科学教育工作者。我们使用自动化开普勒工作流程在HydroFrame社区平台上展示了初步进展。此工作流执行端到端水文模拟，包括数据摄取、预处理、分析、建模和可视化。我们演示了如何不同的模块的工作流程可以重复使用，并重新为三个目标用户组。Kepler工作流程通过内置的来源框架确保完全的可重复性，该框架收集工作流程特定的参数、软件版本和硬件系统配置。此外，我们的目标是优化大规模计算资源的利用率，以适应所有三个用户群的需求。为了实现这一目标，我们提出了一种设计，利用出处数据和机器学习技术来预测性能和预测故障，使用自动性能收集组件的管道。
The HydroFrame project is a community platform designed to facilitate integrated hydrologic modeling across the US. As a part of HydroFrame, we seek to design innovative workflow solutions that create pathways to enable hydrologic analysis for three target user groups: the modeler, the analyzer, and the domain science educator. We present the initial progress on the HydroFrame community platform using an automated Kepler workflow. This workflow performs end-to-end hydrology simulations involving data ingestion, preprocessing, analysis, modeling, and visualization. We demonstrate how different modules of the workflow can be reused and repurposed for the three target user groups. The Kepler workflow ensures complete reproducibility through a built-in provenance framework that collects workflow specific parameters, software versions, and hardware system configuration. In addition, we aim to optimize the utilization of large-scale computational resources to adjust to the needs of all three user groups. Towards this goal, we present a design that leverages provenance data and machine learning techniques to predict performance and forecast failures using an automatic performance collection component of the pipeline.
影响因子：
3.3
作者：
Purawat S;Cowart C;Amaro RE;Altintas I
通讯作者：
Altintas I
影响因子：
3
作者：
Gan Z;Wang J;Salomonis N;Stowe JC;Haddad GG;McCulloch AD;Altintas I;Zambon AC
通讯作者：
Zambon AC