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| 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 | |
| Conference Name | 32nd IEEE Conference on Computer Communications, IEEE INFOCOM 2013 |
| Source | Proceedings - IEEE INFOCOM |
| Pages | 515-519 |
| Conference Date | April 14, 2013 - April 19, 2013 |
| Conference Place | Turin, Italy |
| Indexed Type | EI |
| ISSN | 0743-166X |
| ISBN | 9781467359467 |
| Department | (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 |
| English Abstract | 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. |
| Language | 英语 |
| WOS ID | WOS:000352037600001 |
| Citation statistics | |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/15983 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation 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. |
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