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An Adaptive Partitioning Algorithm for Distributed Discrete Event Simulation Systems

Biocomputing techniques have been proposed to solve combinatorial problems elegantly by such methods as simulated annealing, genetic algorithms and neural networks. In this context, we identify an important optimization problem arising in conservative distributed simulation, such as partitioning, synchronization and communication overhead minimization. We propose the use of a simulated annealing algorithm with an adaptive search schedule to find good (sub-optimal) partitions. The paper discusses the algorithms, its implementation and reports on the performance results of simulation of several workload models on a multiprocessor machine. The results obtained indicate clearly that a partitioning which make use of our simulated annealing significantly reduces the running time of a conservative simulation, and decreases the synchronization overhead of the simulation model when compared to Nandy-Louck's partitioning algorithm.