Abstract
In this paper we present an analysis methodology for the possible improvements of the performance of Adaptive Petri-Net Grid Genetic Algorithm workflow. Genetic Algorithms are very powerful optimization technique that is easily parallelized using different approaches which makes it ideal for the Grid. The High Level Petri-Net workflow model greatly outperforms currently available DAG workflow model available in gLite Grid middleware. Using the flexibility of the High Level Petri-Net workflows we have designed an adaptive workflow that overcomes the heterogeneity and unpredictability of the Grid infrastructure, giving users better and more stable execution times than formerly used DAG workflows. The performance of the Petri-Net Grid Genetic Algorithm is analyzed using several parameters that change the behavior of the optimization. The performance is measured as a shortening of the overall execution time of the workflow in the process of searching for a solution with suitable quality. In the course of the analysis we defined a stable measurement of the quality of the solution used in the experiments. The experimental results obtained by Genetic Algorithm optimization of performance of the Data Warehouse design have shown the unexpected interesting influence some parameters have over the optimization time for obtaining the same quality level.
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Jakimovski, B., Sahpaski, D., Velinov, G. (2010). Optimization of Adaptive Petri-Net Grid Genetic Algorithm Workflows. In: Davcev, D., Gómez, J.M. (eds) ICT Innovations 2009. ICT Innovations 2009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10781-8_41
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DOI: https://doi.org/10.1007/978-3-642-10781-8_41
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