A combined fuzzy-neural network model for non-linear prediction of 3-D rendering workload in grid computing

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
Nikolaos D DoulamisEmmanuel Varvarigos

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 w...Continue Reading

References

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Citations

Apr 15, 2005·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Yongzhi Cao, Mingsheng Ying

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