Examination of load-balancing methods to improve efficiency of a composite materials manufacturing process simulation under uncertainty using distributed computing

Authors:
Feng Zhang;Andryas Mawardi;Eugene Santos, Jr.;Ranga Pitchumani;Luke E. K. Achenie
Affiliations:
Department of Computer Science and Engineering, University of Connecticut, Storrs, CT;Department of Mechanical Engineering, University of Connecticut, Storrs, CT;Thayer School of Engineering, Dartmouth College, Hanover, NH;Department of Mechanical Engineering, University of Connecticut, Storrs, CT;Department of Chemical Engineering, University of Connecticut, Storrs, CT
Venue:
Future Generation Computer Systems - Parallel input/output management techniques (PIOMT) in cluster and grid computing
Year:
2006

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Abstract

Process simulations play an important role in guiding process understanding and development, without requiring costly manufacturing trials. For process design under uncertainty, a large number of simulations is needed for an accurate convergence of the moments of the output distributions, which renders such stochastic analysis computationally intensive. This paper discusses the application of a basic distributed computing approach to reduce the computation time of a composite materials manufacturing process simulation under uncertainty. Specifically, several load-balancing methods are explored and analyzed to determine the best strategies given heterogeneous tasks and heterogeneous networks, especially when the individual task times cannot be predicted.