Abstract

Scientific applications such as those in astronomy, earthquake science, gravitational-wave physics, and others have embraced workflow technologies to do large-scale science. Workflows enable researchers to collaboratively design, manage, and obtain results that involve hundreds of thousands of steps, access terabytes of data, and generate similar amounts of intermediate and final data products. Although workflow systems are able to facilitate the automated generation of data products, many issues still remain to be addressed. These issues exist in different forms in the workflow lifecycle. This chapter describes a workflow lifecycle as consisting of a workflow generation phase where the analysis is defined, the workflow planning phase where resources needed for execution are selected, the workflow execution part, where the actual computations take place, and the result, metadata, and provenance storing …

Date

2012

Authors

Ewa Deelman, Ann Chervenak

Book

Data Intensive Distributed Computing: Challenges and Solutions for Large-scale Information Management

Pages

177-187

Publisher

IGI Global Scientific Publishing