Cloud computing is an emerging computing paradigm that can offer unprecedented scalability and resources on demand, and is getting more and more adoption in the science community, while scientific workflow management systems provide essential support such as management of data and task dependencies, job scheduling and execution, provenance tracking, etc., to scientific computing. As we are entering into a “big data” era, it is imperative to migrate scientific workflow management systems into the cloud to manage the ever increasing data scale and analysis complexity. We propose a reference service framework for integrating scientific workflow management systems into various cloud platforms, which consists of eight major components, including Cloud Workflow Management Service, Cloud Resource Manager, etc., and six interfaces between them. We also present a reference framework for the implementation of Cloud Resource Manager, which is responsible for the provisioning and management of virtual resources in the cloud. We discuss our implementation of the framework by integrating the Swift scientific workflow management system with the OpenNebula and Eucalyptus cloud platforms, and demonstrate the capability of the solution using a NASA MODIS image processing workflow and a production deployment on the Science@Guoshi network with support for the Montage image mosaic workflow.

Scientific workflow management systems (SWFMSs) are facing unprecedented challenges from big data deluge. As revising all the existing workflow applications to fit into Cloud computing paradigm is impractical, thus migrating SWFMSs into the Cloud to leverage the functionalities of both Cloud computing and SWFMSs may provide a viable approach to big data processing. In this paper, we first discuss the challenges for scientific workflow applications and the available solutions in details, and analyze the essential requirements for a scientific computing Cloud platform. Then we propose a service framework to normalize the integration of SWFMS with Cloud computing. Meanwhile, we also present our implementation experience based on the service Framework. At last, we set up a series of experiments to demonstrate the capability of our implementation and use a Montage Image Mosaic Workflow as a showcase of the implementation.