Process-Based Efficient Power Level Exporter

(2004). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Journal of the American Statistical Association: Vol. 99, No. 466, pp. 567-567.

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Data centers are critical, energy-hungry infrastructures that run large-scale Internet-based services. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption in data centers. In this paper, we survey the state-of-the-art techniques used for energy consumption modeling and prediction for data centers and their components. We conduct an in-depth study of the existing literature on data center power modeling, covering more than 200 models. We organize these models in a hierarchical structure with two main branches focusing on hardware-centric and software-centric power models. Under hardware-centric approaches we start from the digital circuit level and move on to describe higher-level energy consumption models at the hardware component level, server level, data center level, and finally systems of systems level. Under the software-centric approaches we investigate power models developed for operating systems, virtual machines and software applications. This systematic approach allows us to identify multiple issues prevalent in power modeling of different levels of data center systems, including: i) few modeling efforts targeted at power consumption of the entire data center ii) many state-of-the-art power models are based on a few CPU or server metrics, and iii) the effectiveness and accuracy of these power models remain open questions. Based on these observations, we conclude the survey by describing key challenges for future research on constructing effective and accurate data center power models.

Data Center Energy Consumption Modeling: A Survey

Virtualization is often used in cloud computing platforms for its several advantages in efficiently managing resources. However, virtualization raises certain additional challenges, and one of them is lack of power metering for virtual machines (VMs). Power management requirements in modern data centers have led to most new servers providing power usage measurement in hardware and alternate solutions exist for older servers using circuit and outlet level measurements. However, VM power cannot be measured purely in hardware. We present a solution for VM power metering, named Joulemeter. We build power models to infer power consumption from resource usage at runtime and identify the challenges that arise when applying such models for VM power metering. We show how existing instrumentation in server hardware and hypervisors can be used to build the required power models on real platforms with low error. Our approach is designed to operate with extremely low runtime overhead while providing practically useful accuracy. We illustrate the use of the proposed metering capability for VM power capping, a technique to reduce power provisioning costs in data centers. Experiments are performed on server traces from several thousand production servers, hosting Microsoft's real-world applications such as Windows Live Messenger. The results show that not only does VM power metering allows virtualized data centers to achieve the same savings that non-virtualized data centers achieved through physical server power capping, but also that it enables further savings in provisioning costs with virtualization.

/pdf/virtual-machine-power-metering-and-provisioning-1yj7epvvd8.pdf

Virtual machine power metering and provisioning

/pdf/ensemble-level-power-management-for-dense-blade-servers-h4jhwl0ddl.pdf

Ensemble-level Power Management for Dense Blade Servers

Actions usually taken to prevent processors from overheating, such as decreasing the frequency or stopping the execution flow, also degrade performance. Multiprocessor systems, however, offer the possibility of moving the task that caused a CPU to overheat away to some other, cooler CPU, so throttling becomes only a last resort taken if all of a system's processors are hot. Additionally, the scheduler can take advantage of the energy characteristics of individual tasks, and distribute hot tasks as well as cool tasks evenly among all CPUs.This work presents a mechanism for determining the energy characteristics of tasks by means of event monitoring counters, and an energy-aware scheduling policy that strives to assign tasks to CPUs in a way that avoids overheating individual CPUs. Our evaluations show that the benefit of avoiding throttling outweighs the overhead of additional task migrations, and that energy-aware scheduling in many cases increases the system's throughput.

/pdf/balancing-power-consumption-in-multiprocessor-systems-42tv7aanxo.pdf

Balancing power consumption in multiprocessor systems

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References

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Data Center Energy Consumption Modeling: A Survey

Virtual machine power metering and provisioning

Ensemble-level Power Management for Dense Blade Servers

Balancing power consumption in multiprocessor systems