Simulation and experimental analysis of data centers in academic campus

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.

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

All-inverter coupled air-side free cooling technology is an energy-saving technology that applies inverter technology to a coupled air conditioning system with liquid pump driven heat pipes and vapor compression refrigeration, allowing it to fully utilize outdoor air cooling resources. In order to comprehensively analyze the energy saving effect of this technology when applied to a data center, we designed and implemented an energy consumption monitoring and testing platform using a data center air-cooled refrigeration air conditioning system as a retrofit and research object. Then, we monitored and analyzed the operating parameters of the heating, ventilation, and air conditioning (HVAC) system before and after the retrofit on this platform, and combined the actual application of this technology in different temperature and altitude regions in China to analyze the energy efficiency of the all-inverter coupled air-side free cooling technology. The results show that: (1) The all-inverter coupled air-side free cooling technology can achieve smooth control of the air conditioning system, reduce power system shock caused by frequent system start-up, and improve system stability. (2) The technology has improved outdoor temperature sensitivity and can make better use of natural outdoor air cooling sources, with energy savings of up to 46.1% when comparing the hottest and coldest seasons. (3) Application results in various regions of China show that the all-inverter coupled air-side free cooling technology is more versatile and achieves good results in both old data center retrofit and new data center construction, with the lowest power usage effectiveness (PUE) value reaching 1.228.

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

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.

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

The main goals of the work presented here were to investigate the energy consumption profiles in data centers and server rooms on the campus of the University of Maryland, College Park, and identify energy conservation measures and the potential savings they can represent. The energy audit process conducted for this study investigated the server rooms’ infrastructure, operational issues, the EnergyConservation Measures (ECMs), and consolidation/co-location opportunities. The identified ECMs were carried out starting with high-reward/low-cost/no-cost investments, such as optimization of the server rooms’ layout, rearrangement of the tile configuration and the racks, removal of unnecessary items and equipment from the data center rooms, increasing the set point temperatures of the cooling units, and installing hot aisle/cold aisle containment. The server rooms were modeled using commercially available software, and temperature sensors and relative humidity sensors used throughout the energy audit study were instrumental in identifying and validating the implemented ECMs. Selected case studies of the audited campus server rooms are also presented in this paper and the learned lessons of their Energy Conservation Measures implementation are also discussed.

Enclosed aisle data centers with bypass recirculation have previously been shown to reduce significantly the cooling infrastructure's power consumption compared to a typical open-aisle configuration. Enclosed aisle configurations also facilitate IT load placement through so-called thermally aware, energy-optimized virtualization. The formidable problem of optimal workload placement, which has received increased interest by the IT industry and has been the subject of numerous investigations, becomes much simpler, almost trivial, when the aisles are enclosed. This is because there is no preference among identical servers in an enclosed aisle based on the thermal environment, which will be uniform in this case, making for a wide range of equivalent load placement possibilities within the data center, or at least those racks in the data center that share enclosed cold aisles. However, the reduction in IT load can have a significant impact on the energy consumption of the cooling infrastructure due to the off-design operation of equipment. The work presented in this paper uses an experimentally validated thermo-hydraulic model of the data center's cooling infrastructure, previously developed by the authors, to explore optimization possibilities in air-cooled, enclosed aisle data centers. The model is used to evaluate the total energy consumption of the data center's cooling infrastructure for data centers operated at reduced IT load. The analysis highlights the importance of reducing the total power required for moving the air within the CRACs, the plenum, and the servers, rather than focusing primarily or exclusively on reducing the refrigeration system's power consumption and shows the benefits of bypass recirculation in enclosed aisle configurations. Energy efficient operating strategies for data centers with enclosed aisles are given for a range of IT loads and ambient conditions.

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.

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

Data center cooling accounts for a significant share of facility energy, yet traditional control strategies often rely on fixed schedules or detailed thermal models that limit adaptability. In this work, we implement a continuous-action deep reinforcement learning (DRL) controller based on the Deep Deterministic Policy Gradient (DDPG) algorithm to adjust supply air, chilled-water, and cooling-tower setpoints in an EnergyPlus simulation of a single-zone data hall. At 15-minute intervals, the agent observes IT load and ambient conditions and learns to minimize non-IT energy consumption (chiller power, fan power, evaporative water) while keeping server-zone outlet temperatures below safety limits. Compared to the default EnergyPlus logic, our DRL controller lowers average Power Usage Effectiveness (PUE) from 1.51 to 1.33 (≈12 %) and reduces peak outlet temperatures from 31.16 °C to 28.33 °C, with tighter performance variability. These results indicate that continuous, model-free DRL can implicitly enforce thermal constraints and exploit cooling subsystem trade-offs without prior calibration. Although evaluated on a simplified lumped-parameter model, this approach shows promise for deployment in heterogeneous or legacy data centers. Future work will extend the framework to multizone configurations, improve sample efficiency via offline pretraining, and incorporate economic factors like water usage and time-of-use electricity pricing.

Multi-objective layout optimization of a generic hybrid-cooled data centre blade server

Energy management and reduction is one of the major challenges in the Cloud industry's data centres. The Uptime Institute's 2012 report, the industry's leading authority, highlighted the crisis in data centre energy management. 30% of data centres will run out of power, space and cooling in 2012. On average there's a 90% energy overhead in data centre operations, yet 55% of servers use less than 10% of their capacity. The 500,000 data centres worldwide have a server electricity consumption of 400 Billion kWh/year (approx $40 Billion ) which is growing at 12% CAGR, yet only 11% of the centres monitor their consumption. The global CO2 emissions of these centres, is equivalent to the airline industry, 2% of global emissions. It is expected that by 2020 20% of Europe's electrical energy will be data centre related. Data centre cooling requirements also have an enormous hydro footprint. A 1 MW data centre in a U.S-type climate has an annual hydro footprint of 99 million litres/annum Yet studies have shown that operational energy savings of 40% and substantial reductions in physical space, cooling requirements and water consumption can be achieved, where comprehensive and effective energy monitoring and management systems are employed. These can deliver a ROI within months, and further savings which extend over the centre's lifetime. The technologies which have the potential to deliver these benefits, DCIM (Data Centre Infrastructure Management) systems have only emerged in the last 3 to 4 years and are still in their infancy. Not surprisingly, a new global industry providing innovative, green, sustainable data centre technologies will be worth €45 Billion by 2016. The main barriers to effective monitoring/management is instrumentation and training. Vast volumes of data are gathered and must be competently analysed. Most monitoring systems, involve some form of physical metering and cabling and downtime, a major challenge when 10's thousands of servers are involved. Furthermore, all approaches physical and hybrid approaches limit the level of monitoring visibility to the server, individual processes cannot be monitored. A spin-out company from University College Dublin Ireland, Stratergia, has developed a unique data centre energy management system PAPILLON. It is totally software-based, measuring every server's energy consumption in real-time. It is intuitive to use, operates on any platform and can be installed in hours without any downtime or retrofitting. It uses a client-server type architecture. Software agents on servers communicate periodically, to a master server which has power models for each server type. Using the power models, the master computes in real-time and saves in a data base, all relevant energy information of the centre. In operation, Papillon identifies and quantifies energy saving actions that can be implemented, and coupled with electricity network data, more sustainable energy sources can be introduced into the power input of the centre. Papillon has been trialled at Swedish Compare Testlab and is one of the core tools being installed at state-of-the-art data centres at Kajaani, Finland and Cenit, Spain for a new international EU-funded Masters programme in green, sustainable data centre management.

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

The accelerating expansion of data centers have heightened the demand for cooling systems that are both efficient and sustainable. This paper examines the performance, safety, and compatibility of three immersion-cooling fluids: a hydrocarbon oil, Thermasolv <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${ }^{\text{TM}}$</tex> CF2 (a fluorinated single-phase fluid), and Thermasolv <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${ }^{\text{TM}}$</tex> IM6 (a fluorinated two-phase fluid). Traditionally, air-cooled systems have had limitations in managing high-density workloads. Air-cooled data centers contribute significantly to energy consumption, water usage, and operational costs. As artificial intelligence (AI), highperformance computing, and hyperscale cloud services expand, two-phase immersion cooling has emerged as a viable alternative. This technology immerses IT hardware in dielectric heat-transfer fluids, allowing for significantly improved thermal management, reduced reliance on mechanical refrigeration, and improved Power Usage Effectiveness (PUE). The favorable characteristics of most dielectric fluids allow for optimal surface contact which contributes to a superior heat transfer performance. Case studies demonstrate that immersion cooling can reduce cooling energy consumption related to air handling systems and pumps by up to 40% and support rack densities exceeding 100 kW which exceed the limits of air-cooled systems. The need for increased data center capacity has made efficient and sustainable two-phase cooling solutions a critical priority.