Optimizing workflow data footprint

Gurmeet Singh, Karan Vahi, Arun Ramakrishnan, Gaurang Mehta, Ewa Deelman, Henan Zhao, Rizos Sakellariou, Kent Blackburn, Duncan Brown, Stephen Fairhurst, David Meyers, G. Bruce Berriman, John Good, Daniel S. Katz

Research output: Contribution to journalArticle

33 Scopus citations

Abstract

In this paper we examine the issue of optimizing disk usage and scheduling large-scale scientific workflows onto distributed resources where the workflows are data-intensive, requiring large amounts of data storage, and the resources have limited storage resources. Our approach is two-fold: we minimize the amount of space a workflow requires during execution by removing data files at runtime when they are no longer needed and we demonstrate that workflows may have to be restructured to reduce the overall data footprint of the workflow. We show the results of our data management and workflow restructuring solutions using a Laser Interferometer Gravitational-Wave Observatory (LIGO) application and an astronomy application, Montage, running on a large-scale production grid-the Open Science Grid. We show that although reducing the data footprint of Montage by 48% can be achieved with dynamic data cleanup techniques, LIGO Scientific Collaboration workflows require additional restructuring to achieve a 56% reduction in data space usage. We also examine the cost of the workflow restructuring in terms of the application's runtime.

Original languageEnglish (US)
Pages (from-to)249-268
Number of pages20
JournalScientific Programming
Volume15
Issue number4
DOIs
StatePublished - 2007

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

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    Singh, G., Vahi, K., Ramakrishnan, A., Mehta, G., Deelman, E., Zhao, H., Sakellariou, R., Blackburn, K., Brown, D., Fairhurst, S., Meyers, D., Berriman, G. B., Good, J., & Katz, D. S. (2007). Optimizing workflow data footprint. Scientific Programming, 15(4), 249-268. https://doi.org/10.1155/2007/701609