Bowman et al 2004a - Revision history

Scipediacontent at 10:41, 22 January 2021

2021-01-22T10:41:51Z

Scipediacontent: Scipediacontent moved page Draft Content 922378561 to Bowman et al 2004a

2020-10-26T12:49:29Z

Scipediacontent moved page Draft Content 922378561 to Bowman et al 2004a

Scipediacontent: Created page with " == Abstract == The visualization of large, remotely located data sets necessitates the development of a distributed computing pipeline in order to reduce the data, in stage..."

2020-10-26T12:49:25Z

Created page with " == Abstract == The visualization of large, remotely located data sets necessitates the development of a distributed computing pipeline in order to reduce the data, in stage..."

New page

== 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.

Document type: Report

== Full document ==
<pdf>Media:Draft_Content_922378561-beopen456-5000-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.2172/841324 http://dx.doi.org/10.2172/841324]

* [http://www.escholarship.org/uc/item/1hp5w4gg http://www.escholarship.org/uc/item/1hp5w4gg]

* [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]

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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.
@@ Line 20: / Line 15: @@
 * [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],

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