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On the Importance of Nonlinear Modeling in Computer Performance Prediction

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8207)

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.

Keywords

Multiple Linear Regression Multiple Linear Regression Model Computer Performance Iterate Function System Prediction Horizon 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.University of ColoradoBoulderUSA

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