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JSSPP
2004
Springer

Parallel Computer Workload Modeling with Markov Chains

14 years 4 months ago
Parallel Computer Workload Modeling with Markov Chains
Abstract. 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.
Baiyi Song, Carsten Ernemann, Ramin Yahyapour
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where JSSPP
Authors Baiyi Song, Carsten Ernemann, Ramin Yahyapour
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