Non-linear 3D rendering workload prediction based on a combined fuzzy-neural network architecture for grid computing applications

Although, computational grid has been initially developed to solve large-scale scientific research problems, it is extended for commercial and industrial applications. An interesting commercial application with a wide impact on a variety of fields, is 3D rendering. In order to implement, however, 3D rendering to a grid infrastructure, we should develop appropriate scheduling and resource allocation mechanisms so that the negotiated quality of service (QoS) requirements are met. Efficient scheduling schemes require modeling and prediction of rendering workload. This is addressed in this paper, based on a combined fuzzy classification and neural network model. Initially, appropriate descriptors are extracted to represent the synthetic world. 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 non-linear input-output relationship between rendering descriptors and the respective computational complexity. To increase the prediction accuracy, a constructive algorithm is adopted in this paper to train the neural network so that network weights and size are simultaneously estimated.