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

Kai Wang, Minghong Lin, Florin Ciucu, Adam Wierman, Chuang Lin
2012 Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems - SIGMETRICS '12  
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
more » ... tatistics 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.
doi:10.1145/2254756.2254815 dblp:conf/sigmetrics/WangLCWL12 fatcat:h2n3n6xtpff3labfsplktvkqsu