Dynamic fuzzy load balancing on LAM/MPI clusters with applications in parallel master-slave implementations of an evolutionary neuro-fuzzy learning system

In the context of parallel master-slave implementations of evolutionary learning in fuzzy-neural network models, a major issue that arises during runtime is how to balance the load-the number of strings assigned to a slave for evaluation during a generation-in order to achieve maximum speed up. Slave evaluation times can fluctuate drastically depending upon the local computational load on the slave (given fixed node specifications). Communication delays compound the problem of proper load assignment. In this paper we propose the design of a novel dynamic fuzzy load estimator for application to load balancing on heterogeneous LAM/MPI clusters. Using average evaluation time and communication delay feedback estimates from slaves, string assignments for evaluation to slaves are dynamically changed during runtime. Extensive tests on heterogenous clusters shows that considerable speedups can be achieved using the proposed fuzzy controller.