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Characterizing the impact of the workload on the value of dynamic resizing in data centers

Wang, Kai and Lin, Minghong and Ciucua, Florin and Wierman, Adam and Lin, Chuang (2013) Characterizing the impact of the workload on the value of dynamic resizing in data centers. In: 2013 Proceedings IEEE INFOCOM. IEEE , Piscataway, NJ, pp. 515-519. ISBN 978-1-4673-5946-7.

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Energy consumption imposes a significant cost for data centers; yet much of that energy is used to maintain excess service capacity during periods of predictably low load. Resultantly, there has recently been interest in developing designs that allow the service capacity to be dynamically resized to match the current workload. However, there is still much debate about the value of such approaches in real settings. In this paper, we show that the value of dynamic resizing is highly dependent on statistics of the workload process. In particular, both slow timescale non-stationarities of the workload (e.g., the peak-to-mean ratio) and the fast time-scale stochasticity (e.g., the burstiness of arrivals) play key roles. To illustrate the impact of these factors, we combine optimization-based modeling of the slow time-scale with stochastic modeling of the fast time scale.

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Additional Information:© 2013 IEEE. This research is supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA06010600), the 973 Program of China (No. 2010CB328105), the NSF grant of China (No. 61020106002), and NSF grant CNS 0846025 and DoE grant DE-EE0002890.
Funding AgencyGrant Number
Chinese Academy of SciencesXDA06010600
973 Program of China2010CB328105
National Natural Scientific Foundation of China61020106002
Department of Energy (DOE)DE-EE0002890
Record Number:CaltechAUTHORS:20170125-151457013
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:73736
Deposited By: Kristin Buxton
Deposited On:25 Jan 2017 23:57
Last Modified:11 Nov 2021 05:21

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