An Evaluation of the Cost and Performance of Scientific Workflows on Amazon EC2

  • Authors:

  • Gideon Juve;Ewa Deelman;G. Bruce Berriman;Benjamin P. Berman;Philip Maechling

  • Affiliations:

  • USC Information Sciences Institute, Marina Del Rey, USA 90292;USC Information Sciences Institute, Marina Del Rey, USA 90292;NASA Exoplanet Science Institute, Infrared, Processing and Analysis Center, Caltech, Pasadena, USA;USC Epigenome Center, Los Angeles, USA;Southern California Earthquake Center, Los Angeles, USA

  • Venue:
  • Journal of Grid Computing
  • Year:
  • 2012

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Abstract

Workflows are used to orchestrate data-intensive applications in many different scientific domains. Workflow applications typically communicate data between processing steps using intermediate files. When tasks are distributed, these files are either transferred from one computational node to another, or accessed through a shared storage system. As a result, the efficient management of data is a key factor in achieving good performance for workflow applications in distributed environments. 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 cloud computing platform. 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.