Model Discovery for Energy-Aware Computing Systems: An Experimental Evaluation

TitleModel Discovery for Energy-Aware Computing Systems: An Experimental Evaluation
Publication TypeConference Paper
Year of Publication2011
AuthorsLi, Zhichao, Grosu Radu, Muppalla Koundinya, Smolka Scott A., Stoller Scott D., and Zadok Erez
Conference Name1st Workshop on Energy Consumption and Reliability of Storage Systems (ERSS '11)
Date Published07/2011
Conference LocationOrlando, FL

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.