Data Management in Scientific Workflows

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 phase. The authors discuss the issues related to data management at each step of the workflow cycle. They describe challenge problems and illustrate them in the context of real-life applications. They discuss the challenges, possible solutions, and open issues faced when mapping and executing large-scale workflows on current cyberinfrastructure. They particularly emphasize the issues related to the management of data throughout the workflow lifecycle.