Optimizing workflow data footprint
Issue title: Dynamic Computational Workflows: Discovery, Optimization and Scheduling
Article type: Research Article
Authors: Singh, Gurmeet | Vahi, Karan | Ramakrishnan, Arun | Mehta, Gaurang | Deelman, Ewa | Zhao, Henan | Sakellariou, Rizos | Blackburn, Kent | Brown, Duncan; | Fairhurst, Stephen; | Meyers, David | Berriman, G. Bruce | Good, John | Katz, Daniel S.
Affiliations: USC Information Sciences Institute, 4676 Admiralty Way, Marina Del Rey, CA 90292, USA. E-mail: {gurmeet,vahi,arun,gmehta,deelman}@isi.edu | School of Computer Science, University of Manchester, Manchester M13 9PL, UK. E-mail: {hzhao,rizos}@cs.man.ac.uk | LIGO Laboratory, California Institute of Technology, MS 18-34, Pasadena, CA 91125, USA. E-mail: {kent,dbrown,fairhurst_s}@ligo.caltech.edu; dpmeyers@usc.edu | Theoretical Astrophysics, California Institute of Technology, MS, 130-33, Pasadena, CA 91125, USA | Physics Department, University of Wisconsin-Milwaukee, Milwaukee, WI 53202, USA | Infrared Processing and Analysis Center, California Institute of Technology, CA 91125, USA. E-mail: {gbb,jcg}@ipac.caltech.edu | Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA. E-mail: dsk@cct.lsu.edu
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.
Journal: Scientific Programming, vol. 15, no. 4, pp. 249-268, 2007
