  Efficient data management is a key component in achieving good performance
for scientific workflows in distributed environments. Workflow applications
typically communicate data between tasks using files. When tasks are
distributed, these files are either transferred from one computational node to
another, or accessed through a shared storage system. In grids and clusters,
workflow data is often stored on network and parallel file systems. In this
paper we investigate some of the ways in which data can be managed for
workflows in the cloud. We ran experiments using three typical workflow
applications on Amazon's EC2. We discuss the various storage and file systems
we used, describe the issues and problems we encountered deploying them on EC2,
and analyze the resulting performance and cost of the workflows.
