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

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.

[1]  Carla E. Brodley,et al.  Predictive application-performance modeling in a computational grid environment , 1999, Proceedings. The Eighth International Symposium on High Performance Distributed Computing (Cat. No.99TH8469).

[2]  Konstantinos Dolkas,et al.  A combined fuzzy-neural network model for non-linear prediction of 3-D rendering workload in Grid computing , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Vipin Kumar,et al.  Information power grid: The new frontier in parallel computing? , 1999, IEEE Concurr..

[4]  Miron Livny,et al.  Condor-a hunter of idle workstations , 1988, [1988] Proceedings. The 8th International Conference on Distributed.

[5]  Graham R. Nudd,et al.  Pace—A Toolset for the Performance Prediction of Parallel and Distributed Systems , 2000, Int. J. High Perform. Comput. Appl..

[6]  Daniel A. Reed,et al.  Performance Contracts: Predicting and Monitoring Grid Application Behavior , 2001, GRID.

[7]  Anastasios Doulamis,et al.  Non-linear prediction of rendering workload for grid infrastructure , 2004 .

[8]  R. F. Freund,et al.  Optimal selection theory for superconcurrency , 1989, Proceedings of the 1989 ACM/IEEE Conference on Supercomputing (Supercomputing '89).

[9]  Subhash Saini,et al.  Local grid scheduling techniques using performance prediction , 2003 .

[10]  Emmanouel A. Varvarigos,et al.  An Advanced Architecture for a Commercial Grid Infrastructure , 2004, European Across Grids Conference.

[11]  Theodora Varvarigou,et al.  Workload Prediction of Rendering Algorithms in GRID Computing , 2001 .

[12]  Arif Ghafoor,et al.  Estimating execution time for parallel tasks in heterogeneous processing (HP) environment , 1994, Proceedings Heterogeneous Computing Workshop.

[13]  Emmanuel Varvarigos,et al.  An Advanced Grid Architecture for a Commercial Grid Infrastructure , 2001 .

[15]  Lee C. Potter,et al.  Statistical prediction of task execution times through analytic benchmarking for scheduling in a heterogeneous environment , 1999, Proceedings. Eighth Heterogeneous Computing Workshop (HCW'99).

[16]  Füsun Özgüner,et al.  Run-time statistical estimation of task execution times for heterogeneous distributed computing , 1996, Proceedings of 5th IEEE International Symposium on High Performance Distributed Computing.

[17]  Stephen A. Jarvis,et al.  Performance-Responsive Middleware for Grid Computing , 2003 .

[18]  Arif Ghafoor,et al.  PAWS: a performance evaluation tool for parallel computing systems , 1991, Computer.

[19]  Ian T. Foster,et al.  The Anatomy of the Grid: Enabling Scalable Virtual Organizations , 2001, Int. J. High Perform. Comput. Appl..