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

ISCAS OpenIR

	characterizing the impact of the workload on the value of dynamic resizing in data centers
	Wang Kai; Lin Minghong; Ciucua Florin; Wierman Adam; Lin Chuang
	2013
会议名称	32nd IEEE Conference on Computer Communications, IEEE INFOCOM 2013
会议录名称	Proceedings - IEEE INFOCOM
页码	515-519
会议日期	April 14, 2013 - April 19, 2013
会议地点	Turin, Italy
收录类别	EI
ISSN	0743-166X
ISBN	9781467359467
部门归属	(1) Institute of Software Chinese Academy of Sciences China; (2) California Institute of Technology United States; (3) T-Labs/TU Berlin Germany; (4) Tsinghua University China
摘要	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 time-scale 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. © 2013 IEEE.; 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 time-scale 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. © 2013 IEEE.
语种	英语
WOS记录号	WOS:000352037600001
引用统计	被引频次：13[WOS] [WOS记录] [WOS相关记录]
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15983
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Wang Kai,Lin Minghong,Ciucua Florin,et al. characterizing the impact of the workload on the value of dynamic resizing in data centers[C],2013:515-519.