2010/10/22 by Gideon Juve, Juve, Gideon, Ewa Deelman +11
Computer Science · Decision Sciences · #Advanced Data Storage Technologies #Distributed #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Parallel #Scientific Computing and Data Management #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1010.4822
openalex publication_date 2010/10/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.