Performance Modeling for Grid-Based Visualization

The visualization of large, remotely located data sets necessitates the development of a distributed computing pipeline in order to reduce the data, in stages, to a manageable size. The Grid offers the baseline infrastructure for launching this distributed pipeline, but it offers few services that support even marginally optimal resource selection and partitioning of the data analysis workflow. We explore a methodology for building a model of overall application performance using a composition of the analytic models of individual components that comprise the pipeline. The analytic models are shown to be accurate on a testbed of distributed heterogeneous systems. The prediction methodology will form the foundation of a more robust resource management service for future Grid-based visualization applications.

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