CMDP based adaptive power management in server clusters

Many high-end computing systems use a large number of power-hungry commercial components to improve performance. Thus power reduction and energy conservation are important in these systems for the purpose of reducing operating costs. Two mechanisms are commonly applied to power reduction in these systems: Dynamic Voltage/Frequency Scaling (DV/FS) and server ON/OFF controlling: Vary-On Vary-Off (VOVF). Previous work addressed the power management issue as an optimization problem and utilized these two mechanisms either separately or together to reduce power consumption. In this paper, we construct a Constrained Markov Decision Process (CMDP) model and propose a CMDP based adaptive power management strategy in web server clusters. Our proposed strategy can greatly reduce the online computation time through an offline initialization process, the online adaptive server adjustment process will further improve system performance and deal with the dynamic changes of workload. It also combines merits from both DV/FS and VOVF mechanisms for better power reduction. The transition overhead caused by server allocation and frequency adjustment is taken into consideration in this work. The CMDP based adaptive power management strategy provides a controllable, predictable, and quantitative control over power consumption with theoretically guaranteed service performance. It is further evaluated via extensive simulations and justified by real workload trace data. The results illustrate the effectiveness and efficiency of our proposed work.