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

• Published in: IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 34; no. 2; pp. 1235 - 1247
• Main Authors: Doulamis, N.D., Doulamis, A.D., Panagakis, A., Dolkas, K., Varvarigou, T.A., Varvarigos, E.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2004
• Subjects: Accuracy; Fuzzy neural networks; Grid computing; Neural networks; Predictive models; Processor scheduling; Quality management; Quality of service; Rendering (computer graphics); Resource management
• ISSN: 1083-4419; 1941-0492
• DOI: 10.1109/TSMCB.2003.822282

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 (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 (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. The 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 in which the tasks should be executed and the most appropriate 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.