A comparison of receiver-initiated and sender-initiated adaptive load sharing
Performance Evaluation
A Dynamic Load-Balancing Policy with a Central Job Dispatcher (LBC)
IEEE Transactions on Software Engineering
IEEE Transactions on Parallel and Distributed Systems
The globus project: a status report
Future Generation Computer Systems - Special issue on metacomputing
PUNCH: An architecture for Web-enabled wide-area network-computing
Cluster Computing
Methodical Analysis of Adaptive Load Sharing Algorithms
IEEE Transactions on Parallel and Distributed Systems
Scheduling Strategies for Master-Slave Tasking on Heterogeneous Processor Platforms
IEEE Transactions on Parallel and Distributed Systems
Heuristic scheduling for bag-of-tasks applications in combination with QoS in the computational grid
Future Generation Computer Systems - Special issue: Advanced grid technologies
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
Grid load balancing using intelligent agents
Future Generation Computer Systems
An adaptive grid implementation of DNA sequence alignment
Future Generation Computer Systems
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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.