Data sharing options for scientific workflows on Amazon EC2

Gideon Juve*, Ewa Deelman, Karan Vahi, Gaurang Mehta, Bruce Berriman, Benjamin P. Berman, Phil Maechling

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

90 Scopus citations

Abstract

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.

Original languageAmerican English
Title of host publication2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2010
DOIs
StatePublished - 2010
Externally publishedYes
Event2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2010 - New Orleans, LA, United States
Duration: 13 Nov 201019 Nov 2010

Publication series

Name2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2010

Conference

Conference2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2010
Country/TerritoryUnited States
CityNew Orleans, LA
Period13/11/1019/11/10

Keywords

  • Cloud computing
  • Cost evaluation
  • Performance evaluation
  • Scientific workflows

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