Summary
In all areas of engineering, it is important to be able to accurately forecast how a system would react to some change. For instance, for a proposed new bridge design, what would be the effect of 10 large trucks simultaneously crossing the bridge? Or, for an existing parallel computer system, what would be the expected response time if three processors failed simultaneously? To be able to answer such performance prediction questions, an engineer needs a correct characterization of the current system.
In this chapter, we apply genetic algorithms (GAs) to the problem of correctly characterizing the workload of computer systems. The GA identifies both the number of workload classes and the class centroids. The proposed new technique appears to outperform K-means clustering, an accepted workload characterization technique.
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© 1997 Springer-Verlag Berlin Heidelberg
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Pettey, C.C., White, P., Dowdy, L., Burkhead, D. (1997). The Identification and Characterization of Workload Classes. In: Dasgupta, D., Michalewicz, Z. (eds) Evolutionary Algorithms in Engineering Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-03423-1_9
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DOI: https://doi.org/10.1007/978-3-662-03423-1_9
Publisher Name: Springer, Berlin, Heidelberg
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