We present a technique that controls the peak power consumption of a high-density server by implementing a feedback controller that uses precise, system-level power measurement to periodically select the highest performance state while keeping the system within a fixed power constraint. A control theoretic methodology is applied to systematically design this control loop with analytic assurances of system stability and controller performance, despite unpredictable workloads and running environments. In a real server we are able to control power over a 1 second period to within 1 W. Additionally, we have observed that power over an 8 second period can be controlled to within 0.1 W. We believe that we are the first to demonstrate such precise control of power in a real server.Conventional servers respond to power supply constraint situations by using simple open-loop policies to set a safe performance level in order to limit peak power consumption. We show that closed-loop control can provide higher performance under these conditions and test this technique on an IBM BladeCenter HS20 server. Experimental results demonstrate that closed-loop control provides up to 82% higher application performance compared to open-loop control and up to 17% higher performance compared to a widely used ad-hoc technique.

We present a technique that controls the peak power consumption of a high-density server by implementing a feedback controller that uses precise, system-level power measurement to periodically select the highest performance state while keeping the system within a fixed power constraint. A control theoretic methodology is applied to systematically design this control loop with analytic assurances of system stability and controller performance, despite unpredictable workloads and running environments. In a real server we are able to control power over a 1 second period to within 1 W and over an 8 second period to within 0.1 W.Conventional servers respond to power supply constraint situations by using simple open-loop policies to set a safe performance level in order to limit peak power consumption. We show that closed-loop control can provide higher performance under these conditions and implement this technique on an IBM BladeCenter HS20 server. Experimental results demonstrate that closed-loop control provides up to 82% higher application performance compared to open-loop control and up to 17% higher performance compared to a widely used ad-hoc technique.

According to standard procedure, building a classi"er using machine learning is a fully automated process that follows the preparation of training data by a domain expert. In contrast, interactive machine learning engages users in actually generating the classi"er themselves. This o!ers a natural way of integrating background knowledge into the modelling stage*as long as interactive tools can be designed that support e$cient and e!ective communication. This paper shows that appropriate techniques can empower users to create models that compete with classi"ers built by state-of-the-art learning algorithms. It demonstrates that users*even users who are not domain experts*can often construct good classi"ers, without any help from a learning algorithm, using a simple two-dimensional visual interface. Experiments on real data demonstrate that, not surprisingly, success hinges on the domain: if a few attributes can support good predictions, users generate accurate classi"ers, whereas domains with many high-order attribute interactions favour standard machine learning techniques. We also present an arti"cial example where domain knowledge allows an &&expert user'' to create a much more accurate model than automatic learning algorithms. These results indicate that our system has the potential to produce highly accurate classi"ers in the hands of a domain expert who has a strong interest in the domain and therefore some insights into how to partition the data. Moreover, small expert-de"ned models o!er the additional advantage that they will generally be more intelligible than those generated by automatic techniques.

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The IBM POWER6e microprocessor chip supports advanced, dynamic power management solutions for managing not just the chip but the entire server. The design facilitates a programmable power management solution for greater flexibility and integration into system-and data-center-wide management solutions. The design of the POWER6 microprocessor provides real-time access to detailed and accurate information on power, temperature, and performance. Together, the sensing, actuation, and management support available in the POWER6 processor, known as the EnergyScalee architecture, enables higher performance, greater energy efficiency, and new power management capabilities such as power and thermal capping and power savings with explicit performance control. This paper provides an overview of the innovative design of the POWER6 processor that enables these advanced, dynamic system power management solutions.