Green Data Centers: A Survey, Perspectives, and Future Directions <s> 5) Carbon Usage Effectiveness (CUE): <s> Self-benchmarking Guide for Data Centers: Metrics, Benchmarks, Actions Paul Mathew, Ph.D. Srirupa Ganguly Steve Greenberg, P.E. Dale Sartor, P.E. Lawrence Berkeley National Laboratory Berkeley, California Prepared for: New York State Energy Research and Development Authority 13 July 2009 <s> BIB001 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> 5) Carbon Usage Effectiveness (CUE): <s> PUE (Power Usage Effectiveness) is a metric developed by the Green Grid widely accepted for measuring energy efficiency in Data Centers (DC). In summary, this metric relates all the energy consumed by the DC by that consumed by the servers. “Where to measure” will depend on the particular characteristics of each facility. “What” and “how” to measure is the goal of this paper. In its most simplified form, PUE is the ratio between annual energy consumed by the DC and annual energy consumed by the servers. There are recommendations as to take measurements in shorter time periods (monthly, weekly, daily, etc‥), whose results may show the evolution of efficiency with time and give greater understanding of the behavior of the DC to take corrective actions. In this work a measurement method will be treated based on the record of the power demand integrated in 15 min or 1 h periods, due to the ease of using standard measuring equipment to obtain it, the reliability and accuracy of data and usefulness of their results. <s> BIB002 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> 5) Carbon Usage Effectiveness (CUE): <s> As data centers proliferate in size and number, the endeavor to improve their energy efficiency and productivity is becoming increasingly important. We discuss the properties of a number of the proposed metrics of energy efficiency and productivity. In particular, we focus on the Data Center Energy Productivity (DCeP) metric, which is the ratio of useful work produced by the data center to the energy consumed performing that work. We describe our approach for using DCeP as the principal outcome of a designed experiment using a highly instrumented, high-performance computing data center. We found that DCeP was successful in clearly distinguishing different operational states in the data center, thereby validating its utility as a metric for identifying configurations of hardware and software that would improve (or even maximize) energy productivity. We also discuss some of the challenges and benefits associated with implementing the DCeP metric, and we examine the efficacy of the metric in making comparisons within a data center and among data centers. <s> BIB003
The total carbon emissions include direct greenhouse gas emissions and indirect greenhouse gas emissions from the consumption of purchased electricity, heat, and steam. The denominator is the energy consumption of the IT equipment  . In addition to these metrics, many other metrics have been proposed by The Green Grid and Lawrence Berkeley National Laboratory that have different practical uses, such as the CEF (Carbon Emission Factor), AEU (Air Economizer Utilization), WEU (Water Economizer Utilization) and CSE (Cooling System Efficiency) BIB001 ,  . Table VII presents the taxonomy of the green metrics listed above. Note that the present values shown in the table were obtained from BIB001 ,  if no other reference is indicated. Now, we focus on works that have studied how to measure these metrics. A technical report BIB001 provided an approach for obtaining the particular values of the metrics. The specific implementation process for benchmarking a data center is illustrated in Fig. 2 . The authors of this report also provided guidance for calculating the required data. For example, electrical energy use can be derived from meter data or utility bills. In BIB002 , the authors' intent was to present the "what" and "how" for the measurement of metrics of energy efficiency. They attempted to introduce a cheap and effective continuous measurement method based on the power demand profiles recorded at various points in a facility. Their power measuring point system is illustrated in Fig. 3 . When the required data have been obtained from the various registries, the metrics can be calculated based on the power consumed integrated over 15-minute or 1-hour periods. By using standard measuring equipment, the reliability and accuracy of the results can be guaranteed. Another work  adopted machine learning techniques to study the PUE. The authors developed a neural network framework that learned from actual operations data to predict the PUE. They reported that the PUE was within 0.004+/-0.005, with an error of approximately 0.4% for a PUE of 1.1. DCeP, the metric of energy productivity, was studied in BIB003 . The authors presented an approach to calculating the DCeP by designing an experiment using a highly instrumented, high-performance computing data center. Their evaluation results demonstrated that the DCeP could be used to successfully distinguish among different operational states in the data center and validated its utility as a metric for identifying the configurations of hardware and software that could improve energy productivity. Unfortunately, the improvement of one specific green metric may lead to the worsening of another metric. Therefore, holistic frameworks for addressing all metrics have also been proposed in recent works  ,  . These approaches offer a combined visualization of all metrics to guide data center operators in worst-metric improvement. 