@conference {kanev2014tradeoffs,
	title = {Tradeoffs between power management and tail latency in warehouse-scale applications},
	booktitle = {2014 IEEE International Symposium on Workload Characterization (IISWC)},
	year = {2014},
	pages = {31{\textendash}40},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The growth in datacenter computing has increased the importance of energy-efficiency in servers. Techniques to reduce power have brought server designs close to achieving energy-proportional computing. However, they stress the inherent tradeoff between aggressive power management and quality of service (QoS) - the dominant metric of performance in datacenters. In this paper, we characterize this tradeoff for 15 benchmarks representing workloads from Google{\textquoteright}s datacenters. We show that 9 of these benchmarks often toggle their cores between short bursts of activity and sleep. In doing so, they stress sleep selection algorithms and can cause tail latency degradation or missed potential for power savings of up to 10\% on important workloads like web search. However, improving sleep selection alone is not sufficient for large efficiency gains on current server hardware. To guide the direction needed for such large gains, we profile datacenter applications for susceptibility to dynamic voltage and frequency scaling (DVFS). We find the largest potential in DVFS which is cognizant of latency/power tradeoffs on a workload-per-workload basis.},
	url = {https://doi.org/10.1109/IISWC.2014.6983037},
	author = {Svilen Kanev and Kim Hazelwood and Gu  Wei and David Brooks}
}
