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Issue No. 02 - February (2011 vol. 22)
ISSN: 1045-9219
pp: 245-259
Xiaorui Wang , University of Tennessee, Knoxville
Yefu Wang , University of Tennessee, Knoxville
Today's data centers face two critical challenges. First, various customers need to be assured by meeting their required service-level agreements such as response time and throughput. Second, server power consumption must be controlled in order to avoid failures caused by power capacity overload or system overheating due to increasing high server density. However, existing work controls power and application-level performance separately, and thus, cannot simultaneously provide explicit guarantees on both. In addition, as power and performance control strategies may come from different hardware/software vendors and coexist at different layers, it is more feasible to coordinate various strategies to achieve the desired control objectives than relying on a single centralized control strategy. This paper proposes Co-Con, a novel cluster-level control architecture that coordinates individual power and performance control loops for virtualized server clusters. To emulate the current practice in data centers, the power control loop changes hardware power states with no regard to the application-level performance. The performance control loop is then designed for each virtual machine to achieve the desired performance even when the system model varies significantly due to the impact of power control. Co-Con configures the two control loops rigorously, based on feedback control theory, for theoretically guaranteed control accuracy and system stability. Empirical results on a physical testbed demonstrate that Co-Con can simultaneously provide effective control on both application-level performance and underlying power consumption.
Power control, power management, performance, virtualization, server clusters, data centers, control theory.

X. Wang and Y. Wang, "Coordinating Power Control and Performance Management for Virtualized Server Clusters," in IEEE Transactions on Parallel & Distributed Systems, vol. 22, no. , pp. 245-259, 2010.
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