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Computational workload prediction for grid oriented industrial applications: The case of 3D-image rendering

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dc.contributor.author Litke, A en
dc.contributor.author Tserpes, K en
dc.contributor.author Varvarigou, T en
dc.date.accessioned 2014-03-01T02:43:11Z
dc.date.available 2014-03-01T02:43:11Z
dc.date.issued 2005 en
dc.identifier.uri http://hdl.handle.net/123456789/31275
dc.subject 3d imaging en
dc.subject Artificial Neural Network en
dc.subject Business Process en
dc.subject Cost Effectiveness en
dc.subject Industrial Application en
dc.subject Large Scale en
dc.subject Open Source en
dc.subject Open Source Software en
dc.subject.other Computation theory en
dc.subject.other Computer software en
dc.subject.other Image processing en
dc.subject.other Neural networks en
dc.subject.other Problem solving en
dc.subject.other Three dimensional en
dc.subject.other Computing intensive problems en
dc.subject.other Grid infrastructure en
dc.subject.other Industrial users en
dc.subject.other Software modules en
dc.subject.other Industrial applications en
dc.title Computational workload prediction for grid oriented industrial applications: The case of 3D-image rendering en
heal.type conferenceItem en
heal.identifier.primary 10.1109/CCGRID.2005.1558665 en
heal.identifier.secondary http://dx.doi.org/10.1109/CCGRID.2005.1558665 en
heal.identifier.secondary 1558665 en
heal.publicationDate 2005 en
heal.abstract Grids are typically used for solving large-scale resource and computing intensive problems in science, engineering, and commerce as they seem to be cost-effective for industrial users. In order to be able to meet this requirement the software modules developed should be designed to meet the requisites for commercial business processes on the Grid. In this paper we present a module for predicting computational workload of jobs assigned for execution on commercially exploited Grid infrastructures. The module aims to identify the complexity of a given job and predict the workload that it is going to stress on the Grid infrastructure. The prediction is achieved with the use of a trained Artificial Neural Network which has been implemented with the use of the open source software package Joone. The approach has been implemented and validated within the framework of GRIA IST project for a specific industrial based application namely, 3D image rendering. The evaluation of the approach showed very promising results not only for the adoption of an open source package in a commercial application but also concerning the accuracy of the prediction and the benefit that it can provide in Grids for business. © 2005 IEEE. en
heal.journalName 2005 IEEE International Symposium on Cluster Computing and the Grid, CCGrid 2005 en
dc.identifier.doi 10.1109/CCGRID.2005.1558665 en
dc.identifier.volume 2 en
dc.identifier.spage 962 en
dc.identifier.epage 969 en


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