Abstract
Computers are nonlinear dynamical systems that exhibit complex and sometimes even chaotic behavior. The low-level performance models used in the computer systems community, however, are linear. This paper is an exploration of that disconnect: when linear models are adequate for predicting computer performance and when they are not. Specifically, we build linear and nonlinear models of the processor load of an Intel i7-based computer as it executes a range of different programs. We then use those models to predict the processor loads forward in time and compare those forecasts to the true continuations of the time series.
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Garland, J., Bradley, E. (2013). On the Importance of Nonlinear Modeling in Computer Performance Prediction. In: Tucker, A., Höppner, F., Siebes, A., Swift, S. (eds) Advances in Intelligent Data Analysis XII. IDA 2013. Lecture Notes in Computer Science, vol 8207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41398-8_19
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DOI: https://doi.org/10.1007/978-3-642-41398-8_19
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