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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. | 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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* [https://digital.library.unt.edu/ark:/67531/metadc781465/m2/1/high_res_d/841324.pdf https://digital.library.unt.edu/ark:/67531/metadc781465/m2/1/high_res_d/841324.pdf] | * [https://digital.library.unt.edu/ark:/67531/metadc781465/m2/1/high_res_d/841324.pdf https://digital.library.unt.edu/ark:/67531/metadc781465/m2/1/high_res_d/841324.pdf] | ||
| − | * [https://escholarship.org/uc/item/1hp5w4gg https://escholarship.org/uc/item/1hp5w4gg],[https://core.ac.uk/display/9088645 https://core.ac.uk/display/9088645],[https://digital.library.unt.edu/ark:/67531/metadc781465 https://digital.library.unt.edu/ark:/67531/metadc781465],[https://academic.microsoft.com/#/detail/1557501604 https://academic.microsoft.com/#/detail/1557501604] | + | * [https://escholarship.org/uc/item/1hp5w4gg https://escholarship.org/uc/item/1hp5w4gg], |
| + | : [https://escholarship.org/uc/item/1hp5w4gg.pdf https://escholarship.org/uc/item/1hp5w4gg.pdf], | ||
| + | : [https://core.ac.uk/display/9088645 https://core.ac.uk/display/9088645], | ||
| + | : [https://digital.library.unt.edu/ark:/67531/metadc781465 https://digital.library.unt.edu/ark:/67531/metadc781465], | ||
| + | : [https://www.osti.gov/servlets/purl/841324 https://www.osti.gov/servlets/purl/841324], | ||
| + | : [https://academic.microsoft.com/#/detail/1557501604 https://academic.microsoft.com/#/detail/1557501604] | ||
Latest revision as of 11:41, 22 January 2021
Abstract
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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Published on 01/01/2004
Volume 2004, 2004
DOI: 10.2172/841324
Licence: CC BY-NC-SA license
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