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2011 International Green Computing Conference and Workshops (2011)
Orlando, FL
July 25, 2011 to July 28, 2011
ISBN: 978-1-4577-1222-7
pp: 1-6
Zhichao Li , Dept. of Comput. Sci., Stony Brook Univ., Stony Brook, NY, USA
R. Grosu , Dept. of Comput. Sci., Stony Brook Univ., Stony Brook, NY, USA
K. Muppalla , Dept. of Comput. Sci., Stony Brook Univ., Stony Brook, NY, USA
S. A. Smolka , Dept. of Comput. Sci., Stony Brook Univ., Stony Brook, NY, USA
S. D. Stoller , Dept. of Comput. Sci., Stony Brook Univ., Stony Brook, NY, USA
E. Zadok , Dept. of Comput. Sci., Stony Brook Univ., Stony Brook, NY, USA
ABSTRACT
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.
INDEX TERMS
SISO model, energy aware computing system, model discovery methodology, energy performance trade off, multiple inputs multiple outputs model, single input single output model, representative server workload, MIMO model
CITATION

S. D. Stoller, R. Grosu, Zhichao Li, E. Zadok, K. Muppalla and S. A. Smolka, "Model discovery for energy-aware computing systems: An experimental evaluation," 2011 International Green Computing Conference and Workshops(IGCC), Orlando, FL, 2011, pp. 1-6.
doi:10.1109/IGCC.2011.6008572
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