Chaotic Attractor Prediction for Server Run-time Energy Consumption

This paper proposes a chaotic time series model of server systemwide energy consumption to capture the dynamics present in observed sensor readings of underlying physical systems. Based on the chaotic model, we have developed a real-time predictor that estimates actual server energy consumption according to its overall thermal envelope. This chaotic time series regression model relates processor power, bus activity, and system ambient temperatures for real-time prediction of power consumption during job execution to enable run-time control of their thermal impacts. An experimental case study compares our Chaotic Attractor Predictor (CAP) against previous prediction models constructed according to other statistical methods. Our CAP is found to be accurate within an average error of 2% (or 7%) and the worst case error of 7% (or 20%) for the AMD Opteron processor (or for the Intel Nehalem processor), based on executing a set of SPEC CPU2006 benchmarks.