Model-Driven Simulation of Grid Scheduling Strategies

Simulation studies of grid scheduling strategies require representative workloads to produce dependable results. Real production grid workloads have shown diverse correlation structures and scaling behavior, which are different than the characteristics of the available supercomputer workloads and cannot be captured by Poisson or simple distribution-based models. We present models that are able to reproduce various correlation structures, including pseudo-periodicity and long range dependence. By conducting model-driven simulation, we quantitatively evaluate the performance impacts of workload correlations in grid scheduling. The results indicate that autocorrelations in workloads result in worse system performance, both at the local and the grid level. It is shown that realistic workload modeling is not only possible, but also necessary to enable dependable grid scheduling studies.

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