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A new framework automatically generates full-system multicore powermarks, or synthetic programs with desired power characteristics on multicore server platforms. The framework constructs full-system power models with error bounds on the power estimates and guides the design of energy-efficient and cost-efficient server and data center infrastructures.
Energy efficiency of large-scale data centers is becoming a major concern not only for reasons of energy conservation, failures, and cost reduction, but also because such sys tems are soon reaching the limits of power available to them. Like High Performance Computing (HPC) systems, large-scale clu ster-based data centers can consume power in megawatts, and of all the power consumed by such a system,...
In this paper we present a system for online power prediction in vir-tualized environments. It is based on Gaussian mixture models that use architectural metrics of the physical and virtual machines (VM) collected dynamically by our system to predict both the physical machine and per VM level power consumption. A real implementation of our system shows that it can achieve average prediction error...
In order to meet the requirements of both low power consumption and high performance in embedded server systems, a single IP address cluster of low power embedded CPUs can be built, based on the following policy: the number of CPUs increases when high performance services are required, while the number of CPUs decreases when there are few requests. A cluster of embedded SH7780 CPUs, called the SH...
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