Characterizing the impact of the workload on the value of dynamic resizing in data centers
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. Within this framework, we provide both analytic and numerical results characterizing when dynamic resizing does (and does not) provide benefits.
© 2014 Elsevier B.V. Received 18 May 2013, Revised 6 October 2014, Accepted 15 December 2014, Available online 23 December 2014. This research is supported by the NSF grant of China (No. 61303058), the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA06010600), the 973 Program of China (No. 2010CB328105), and NSF grant Computer and Network Systems 0846025 and DoE grant DE-EE0002890.