Parallel computer workload modeling with Markov chains

In order to evaluate different scheduling strategies for parallel computers, simulations are often executed. As the scheduling quality highly depends on the workload that is served on the parallel machine, a representative workload model is required. Common approaches such as using a probability distribution model can capture the static feature of real workloads, but they do not consider the temporal relation in the traces. In this paper, a workload model is presented which uses Markov chains for modeling job parameters. In order to consider the interdependence of individual parameters without requiring large scale Markov chains, a novel method for transforming the states in different Markov chains is presented. The results show that the model yields closer results to the real workloads than other common approaches.