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Abstract: Cloud computing is an emerging computing paradigm that can offer unprecedented scalability and resources on demand, and is gaining significant adoption in the science community. At the same time, scientific workflow man-agement systems provide essential support and functionality to scientific compu-ting, such as management of data and task dependencies, job scheduling and exe-cution, provenance tracking, fault tolerance. Migrating scientific workflow management systems from traditional Grid computing environments into the Cloud would enable a much broader user base to conduct their scientific research with ever increasing data scale and analysis complexity. This paper presents our experience in integrating the Swift scientific workflow management system with the OpenNebula Cloud platform, which supports workflow specification and sub-mission, on-demand virtual cluster provisioning, high-throughput task scheduling and execution, and efficient and scalable resource management in the Cloud. We set up a series of experiments to demonstrate the capability of our integration and use a MODIS image processing workflow as a showcase of the implementation