@conference {62,
	title = {Model Discovery for Energy-Aware Computing Systems: An Experimental Evaluation},
	booktitle = {1st Workshop on Energy Consumption and Reliability of Storage Systems (ERSS {\textquoteright}11)},
	year = {2011},
	month = {07/2011},
	address = {Orlando, FL},
	abstract = {<div>We present a model-discovery methodology for energy-aware computing systems that achieves high prediction accuracy. Model discovery, or system identification, is a critical first step in designing advanced controllers that can dynamically manage the energy-performance trade-off in an optimal manner. Our methodology favors Multiple-Inputs-Multiple-Outputs (MIMO) models over a collection of Single-Input-Single-Output (SISO) models, when the inputs and outputs of the system are coupled in a nontrivial way. In such cases, MIMO is generally more accurate than SISO over a wide range of inputs in predicting system behavior. Our experimental evaluation, carried out on a representative server workload, validates our approach. We obtained an average prediction accuracy of 77\% and 76\% for MIMO power and performance, respectively. We also show that MIMO models are consistently more accurate than SISO ones.</div>
},
	url = {http://www.fsl.cs.stonybrook.edu/docs/erss11model/erss11model.pdf},
	author = {Zhichao Li and Radu Grosu and Koundinya Muppalla and Scott A. Smolka and Scott D. Stoller and Erez Zadok}
}
