Abstract
We present algorithms for characterizing the demand behavior of applications and predicting demand by mining periodicities in historical data. Our algorithms are change-adaptive, automatically adjusting to new regularities in demand patterns while maintaining low algorithm running time. They are intended for applications in scientific computing clusters, enterprise data centers, and Grid and Utility environments that exhibit periodical behavior and may benefit significantly from automation. A case study incorporating data from an enterprise data center is used to evaluate the effectiveness of our technique.
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