Scaling Up Data-Centric Middleware on a Cluster Computer

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Data-centric workflow middleware systems are workflow systems that treat data as first class objects alongside programs. These systems improve the usability, responsiveness and efficiency of workflow execution over cluster (and grid) computers. In this work, we explore the scalability of one such system, GridDB, on cluster computers. We measure the performance and scalability of GridDB in executing data-intensive image processing workflows from the SuperMACHO astrophysics survey on a large cluster computer. Our first experimental study concerns the scale-up of GridDB. We make a rather surprising finding, that while the middleware system issues many queries and transactions to a DBMS, file ... continued below

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PDF-file: 14 pages; size: 0.3 Mbytes

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Liu, D T; Franklin, M J; Garlick, J & Abdulla, G M April 29, 2005.

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Data-centric workflow middleware systems are workflow systems that treat data as first class objects alongside programs. These systems improve the usability, responsiveness and efficiency of workflow execution over cluster (and grid) computers. In this work, we explore the scalability of one such system, GridDB, on cluster computers. We measure the performance and scalability of GridDB in executing data-intensive image processing workflows from the SuperMACHO astrophysics survey on a large cluster computer. Our first experimental study concerns the scale-up of GridDB. We make a rather surprising finding, that while the middleware system issues many queries and transactions to a DBMS, file system operations present the first-tier bottleneck. We circumvent this bottleneck and increase the scalability of GridDB by more than 2-fold on our image processing application (up to 128 nodes). In a second study, we demonstrate the sensitivity of GridDB performance (and therefore application performance) to characteristics of the workflows being executed. To manage these sensitivities, we provide guidelines for trading off the costs and benefits of GridDB at a fine-grain.

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PDF-file: 14 pages; size: 0.3 Mbytes

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  • Presented at: Supercomputing 05, Seattle, WA, United States, Nov 12 - Nov 18, 2005

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  • Report No.: UCRL-CONF-212323
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 878189
  • Archival Resource Key: ark:/67531/metadc877329

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  • April 29, 2005

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  • Sept. 21, 2016, 2:29 a.m.

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  • Dec. 7, 2016, 8:50 p.m.

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Liu, D T; Franklin, M J; Garlick, J & Abdulla, G M. Scaling Up Data-Centric Middleware on a Cluster Computer, article, April 29, 2005; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc877329/: accessed September 21, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.