Power usage effectiveness in data centers: overloaded and underachieving

Data centers are energy-intensive facilities, with over 95% of their total cooling load attributed to the heat generated by information technology equipment (ITE). Various energy-saving techniques have been employed to enhance data center efficiency and to reduce power usage effectiveness (PUE). Among these, economizers using outdoor air for cooling are the most effective for addressing year-round cooling demands. Despite the simplicity of the load composition, analyzing data center cooling systems involves dynamic considerations, such as weather conditions, system conditions, and economizer control. A PUE interpretation tool was specifically developed for use in data centers, aimed at addressing the simplicity of data center loads and the complexity of system analysis. The tool was verified through a comparison with results from DesignBuilder implementing the EnergyPlus algorithm. Using the developed tool, a comparative analysis of economizer strategies based on the PUE distribution was conducted, with the aim of reducing the PUE of data centers across various climatic zones. The inclusion of evaporative cooling (EC) further improved cooling efficiency, leading to reductions in PUE by approximately 0.02 to 0.05 in dry zones. Additionally, wet zones exhibited PUE reductions, ranging from approximately 0.03 to 0.07, with the implementation of indirect air-side economizer (IASE). Sensitivity and uncertainty analysis were further conducted. The computer room air handler (CRAH) supply temperature and CRAH temperature difference were the most influential factors affecting the annual PUE. For the direct air-side economizer (DASE) and DASE + EC systems, higher PUE uncertainty was observed in zones 1B, 3B, 4B, and 5B, showing ranges of 1.17–1.39 and 1.15–1.17, respectively. In the case of the IASE and IASE + EC systems, higher PUE uncertainty was noted in zones 0A, 0B, 1A, 1B, and 2A, with ranges of 1.22–1.43 and 1.17–1.43, respectively. The distinctive innovation of the tool developed in this study is characterized by its integration of specific features unique to data centers. It streamlines the computation of cooling loads, thus minimizing the burden of input, and delivers energy consumption data for data center cooling systems with a level of precision comparable to that of commercial dynamic energy analysis tools. It provides data center engineers with a valuable resource to identify optimal alternatives and system design conditions for data centers. This empowers them to make informed decisions based on energy efficiency enhancements, thereby strengthening their ability to improve energy efficiency.

Case study of data centers’ energy performance

A critical analysis of Power Usage Effectiveness and its use in communicating data center energy consumption

Water-energy tradeoffs in data centers: A case study in hot-arid climates

Many popular e-commerce applications run on geo-distributed data centers requiring high availability. Fault-tolerant distributed data centers are designed by provisioning spare compute capacity to support the load of failed data center, apart from ensuring data durability. The main challenge during the planning phase is how to provision spare capacity such that the total cost of ownership (TCO) is minimized. While the literature handled spare capacity provisioning by minimizing the number of servers, variation in electricity cost and PUE corroborate the need to minimize the operating cost for capacity provisioning. We develop an MILP model for spare capacity provisioning for geo-distributed data centers with durability requirements. We consider spare capacity provisioning problem with the objective of minimizing TCO. We model variation in the demand, fluctuation in electricity prices across locations, cost of state replication, carbon tax across different countries, and delay constraints while formulating the optimization model. Solving the model shows that TCO is reduced while leveraging the electricity price variation and demand multiplexing. The proposed model outperforms the CDN model by 50% and the minimum server model by 34%. Results also demonstrate the effect of power usage effectiveness (PUE), latency, number of data centers and demand on the TCO.

With the rise of acceptable operating temperatures for information technology (IT) equipment, using ambient air has been a growing trend for data centers. In this computational study, a thermal wheel is used as a rotary air-to-air heat exchanger extracting heat from IT equipment and dissipating it to the ambient. The wheel is made of a metallic honeycomb material that enables air to pass through while convectively transferring heat. A computational model has been developed to calculate the volumetric air flow rate required for an assigned cooling load. When the ambient temperature is too high, direct expansion cooling is used as a secondary approach and modeled in building energy usage estimation software, EnergyPlus. The integrated computational model calculates overall power usage effectiveness (PUE) for data centers. Using weather data for a specific location, PUEs for several different climates can be obtained. Even without considering the energy savings that is produced through less air quality and humidity control, for a cooling load of 400 kW, it was found that overall PUE can be as low as 1.10 in Helsinki, Finland and 1.20 in Atlanta, Georgia. The presented model can be used to determine the system performance with varying location, cooling load, and regenerative heat exchanger parameters.

Model-based Approach to Data Center Design and Power Usage Effectiveness Assessment

Two-phase liquid-immersion data center cooling system: Experimental performance and thermoeconomic analysis

Energy efficiency and low carbon enabler green IT framework for data centers considering green metrics

Environmental Investment and Firm Performance: A Panel VAR Approach

A novel integrated fuzzy control system toward automated local airflow management in data centers

Investigation on application of plate-tube evaporative condensating technology for data centres in the northwest of China

Simulation and Experimental studies are performed to evaluate the energy efficiency performance, develop energy conservation measures (ECMs), and conduct overall energy analysis of data centers at the campus of the University of Maryland. Based on the analysis, the PUE (power usage effectiveness) of the data center was found to be 2.72, suggesting ample opportunity for energy saving measures. The IT, cooling, and electrical loads consume 38.5%, 43.3%, and 18.1% of the total data center energy consumption, respectively. Three ECMs are investigated to reduce energy consumption: 1) eliminate unnecessary CRAHs (computer room air conditioning units) in RDC (research data center); 2) eliminate unnecessary CRAHs in SDC (secondary data center); and 3) remove block back flow effect.

The modern data centers (DCs) are essential to fulfilling ever-evolving computational demands around cloud computing, big data, and IT infrastructure. These DCs are facilities (Figure 1) that house computer systems and associated components such as networking and storage systems. To operate a DC, power supplies, network connections, environmental controls (e.g., air conditioning, humidity), and security infrastructure are needed. Technology and business challenges such as virtualization, load consolidation, real-time troubleshooting, and service-level guarantees require a robust and adaptive server management plan for enterprise. The majority of DC issues are related to overutilization of resources, application failures, data security, power usage effectiveness (PUE), and infrastructure costs. This requires proactive solutions that are business intelligent and built over a network of sense points that are guaranteed to deliver reliable trends and measurements in a reliable and timely fashion. Since it is expensive to build new DCs, the best option is to improve usage of an existing facility through lower infrastructure overhead to deliver better resource management. An optimal sensor network would perform real-time sensor-data collection and deliver a) improved server rack utilization, b) improved DC cooling, and c) improved loadbalancing through dynamic capping of thermally constrained systems.

The air free-cooling has been long thought infeasible in tropics due to the unique challenges of year-round high ambient temperature and relative humidity. In recent years, the increasing availability of servers that can tolerate higher temperatures and relative humidity levels sheds light upon the feasibility of the air free-cooling to enhance the data center energy efficiency. However, building an air free-cooled data center in the tropics requires extensive experiments to understand the details of how the tropical environment conditions will affect data center power consumption, computing throughput, and server hardware reliability. Thus, together with multiple partners in data center industry and research, we conducted a project that designs, builds, and experiments with an air free-cooled data center testbed consisting of three server rooms hosting 12 server racks with 60 kW total power rating. This paper presents the key observations, experiences and learned lessons obtained from our project. The experiments show that (1) the air free-cooling design that uses fans only can reduce the power usage effectiveness (PUE) by 38%, compared to the global average PUE, (2) the tropics' year-round high temperatures up to 37°C do not impede the air free-cooling, and (3) the implementation of the air free-cooled data centers in tropics requires special cares to deal with airborne contaminants to avoid fast corrosion rate and dust-induced server faults.