Simple and Effective Dynamic Provisioning for Power-Proportional Data Centers

Tan Lu, Minghua Chen

Energy consumption represents a significant cost in data center operation. A large fraction of the energy, however, is used to power idle servers when the workload is low. Dynamic provisioning techniques aim at saving this portion of the energy, by turning off unnecessary servers. In this paper, we explore how much performance gain can knowing future workload information brings to dynamic provisioning. In particular, we study the dynamic provisioning problem under the cost model that a running server consumes a fixed amount energy per unit time, and develop online solutions with and without future workload information available. We first reveal an elegant structure of the off-line dynamic provisioning problem, which allows us to characterize and achieve the optimal solution in a {}"divide-and-conquer" manner. We then exploit this insight to design three online algorithms with competitive ratios $2-\alpha$, $(e-\alpha)/(e-1)\approx1.58-\alpha/(e-1)$ and $e/(e-1+\alpha)$, respectively, where $0\leq\alpha\leq1$ is the fraction of a critical window in which future workload information is available. A fundamental observation is that \emph{future workload information beyond the critical window will not} \emph{improve dynamic provisioning performance}. Our algorithms are decentralized and are simple to implement. We demonstrate their effectiveness in simulations using real-world traces. We also compare their performance with state-of-the-art solutions.

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