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

Litke, A; Tserpes, K; Varvarigou, T

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	https://dspace.lib.ntua.gr/xmlui/handle/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