@inproceedings{TD:100018,
	att_abstract={Dynamic voltage and frequency scaling (DVFS)  and virtual machine (VM) based server consolidation are techniques that hold promise for energy conservation, but can also have adverse impacts on system performance. For the responsiveness-sensitive multitier applications running in today's data centers, queuing models should ideally be used 
to predict the impact of CPU scaling on response time, to allow appropriate runtime trade-offs between performance and energy use. In practice, however, such models are difficult to construct and thus are often abandoned for ad-hoc solutions. In this paper, an alternative measurement-based approach that predicts the impacts without requiring detailed application knowledge is presented. The approach uses a new set of metrics, the CPU gradients, that can be automatically measured on a running system using lightweight and nonintrusive CPU perturbations. The practical feasibility of the approach is demonstrated using extensive experiments on multiple multitier applications, and it is shown that simple energy controllers can use gradient predictions to derive as much as 50% energy 
savings while still meeting response time constraints. 
},
	att_authors={kj2681, mh7921, rs2497},
	att_categories={C_CCF.8, C_NSS.4, C_NSS.5},
	att_copyright={IEEE},
	att_copyright_notice={},
	att_donotupload={true},
	att_private={false},
	att_projects={},
	att_tags={green computing, CPU frequency scaling, virtual machines, resource allocation},
	att_techdoc={true},
	att_techdoc_key={TD:100018},
	att_url={},
	author={Shuyi Chen AND Kaustubh Joshi AND Matti Hiltunen AND Richard Schlichting AND William Sanders},
	booktitle={Proceedings of the 1st IEEE International Green Computing Conference},
	institution={{IEEE International Green Computing Conference}},
	month={August},
	title={{CPU Gradients: Performance-aware Energy Conservation in Multitier Systems}},
	year=2010,
}