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 required baseline infrastructure for launching such a distributed pipeline is becoming available, but few services 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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