@conference {63,
	title = {On the Energy Consumption and Performance of Systems Software},
	booktitle = {4th Israeli Experimental Systems Conference (ACM SYSTOR {\textquoteright}11)},
	year = {2011},
	month = {06/2011},
	address = {Haifa, Israel},
	abstract = {<p>Models of energy consumption and performance are necessary to understand and identify system behavior, prior to designing advanced controls that can balance out performance and energy use. This paper considers the energy consumption and performance of servers running a relatively simple file-compression workload. We found that standard techniques for system identification do not produce acceptable models of energy consumption and performance, due to the intricate interplay between the discrete nature of software and the continuous nature of energy and performance. This motivated us to perform a detailed empirical study of the energy consumption and performance of this system with varying compression algorithms and compression levels, file types, persistent storage media, CPU DVFS levels, and disk I/O schedulers. Our results identify and illustrate factors that complicate the system{\textquoteright}s energy consumption and performance, including nonlinearity, instability, and multi-dimensionality. Our results provide a basis for future work on modeling energy consumption and performance to support principled design of controllable energy-aware systems.</p>
},
	url = {http://www.fsl.cs.stonybrook.edu/docs/erss11model/erss11model.pdf},
	author = {Zhichao L and Radu Grosu and Priya Sehgal and Scott A. Smolka and Scott D. Stoller and Erez Zadok}
}
@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}
}
