A Combined Fuzzy-Neural Network Model for Non-Linear Prediction of 3-D Rendering Workload in Grid Computing

Doulamis, ND; Doulamis, AD; Panagakis, A; Dolkas, K; Varvarigou, TA; Varvarigos, E

dc.contributor.author	Doulamis, ND	en
dc.contributor.author	Doulamis, AD	en
dc.contributor.author	Panagakis, A	en
dc.contributor.author	Dolkas, K	en
dc.contributor.author	Varvarigou, TA	en
dc.contributor.author	Varvarigos, E	en
dc.date.accessioned	2014-03-01T01:19:42Z
dc.date.available	2014-03-01T01:19:42Z
dc.date.issued	2004	en
dc.identifier.issn	10834419	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15680
dc.subject	Grid computing	en
dc.subject	Neural networks	en
dc.subject	Three-dimensional (3-D) rendering	en
dc.subject	Workload prediction	en
dc.subject.other	Algorithms	en
dc.subject.other	Computation theory	en
dc.subject.other	Functions	en
dc.subject.other	Image processing	en
dc.subject.other	Mathematical models	en
dc.subject.other	Neural networks	en
dc.subject.other	Nonlinear systems	en
dc.subject.other	Problem solving	en
dc.subject.other	Quality of service	en
dc.subject.other	Queueing networks	en
dc.subject.other	Ray tracing	en
dc.subject.other	Resource allocation	en
dc.subject.other	Grid computing	en
dc.subject.other	Three-dimensional (3-D) rendering	en
dc.subject.other	Workload prediction	en
dc.subject.other	Fuzzy sets	en
dc.title	A Combined Fuzzy-Neural Network Model for Non-Linear Prediction of 3-D Rendering Workload in Grid Computing	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TSMCB.2003.822282	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TSMCB.2003.822282	en
heal.publicationDate	2004	en
heal.abstract	Implementation of a commercial application to a grid infrastructure introduces new challenges in managing the quality-of-service (QoS) requirements, most stem from the fact that negotiation on QoS between the user and the service provider should strictly be satisfied. An interesting commercial application with a wide impact on a variety of fields, which can benefit from the computational grid technologies, is three-dimensional (3-D) rendering. In order to implement, however, 3-D rendering to a grid infrastructure, we should develop appropriate scheduling and resource allocation mechanisms so that the negotiated (QoS) requirements are met. Efficient scheduling schemes require modeling and prediction of rendering workload. In this paper workload prediction is addressed based on a combined fuzzy classification and neural network model. Initially, appropriate descriptors are extracted to represent the synthetic world, The descriptors are obtained by parsing RIB formatted files, which provides a general structure for describing computer-generated images. Fuzzy classification is used for organizing rendering descriptor so that a reliable representation is accomplished which increases the prediction accuracy. Neural network performs workload prediction by modeling the nonlinear input-output relationship between rendering descriptors and the respective computational complexity. To increase prediction accuracy, a constructive algorithm is adopted in this paper to train the neural network so that network weights and size are simultaneously estimated. Then, a grid scheduler scheme is proposed to estimate the queuing order that the tasks should be executed and the most appopriate processor assignment so that the demanded QoS are satisfied as much as possible. A fair scheduling policy is considered as the most appropriate. Experimental results on a real grid infrastructure are presented to illustrate the efficiency of the proposed workload prediction - scheduling algorithm compared to other approaches presented in the literature.	en
heal.journalName	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics	en
dc.identifier.doi	10.1109/TSMCB.2003.822282	en
dc.identifier.volume	34	en
dc.identifier.issue	2	en
dc.identifier.spage	1235	en
dc.identifier.epage	1247	en