Energy Optimization of Air-Cooled Data Centers

2010 ◽  
Vol 2 (4) ◽  
Author(s):  
H. E. Khalifa ◽  
D. W. Demetriou
Keyword(s):  
Power Consumption ◽  
Temperature Rise ◽  
Data Center ◽  
Data Centers ◽  
Optimal Operation ◽  
Effect Of Temperature ◽  
Cooling Power ◽  
Air Conditioners ◽  
Air Supply ◽  
Operating Strategies

The work presented in this paper describes a simplified thermodynamic model that can be used for exploring optimization possibilities in air-cooled data centers. The model has been used to identify optimal, energy-efficient designs, operating scenarios, and operating parameters such as flow rates and air supply temperatures. The results of this analysis highlight the important features that need to be considered when optimizing the operation of air-cooled data centers, especially the trade-off between low air supply temperature and increased air flow rate. The model was shown to be especially valuable in defining the optimal operating strategies for enclosed aisle configurations with fixed and variable server flows, and to elucidate the deleterious effect of temperature nonuniformity at the inlet of the racks on the data center cooling infrastructure power consumption. The analysis shows a potential for as much as an ∼58% savings in cooling infrastructure energy consumption by utilizing an optimized enclosed aisle configuration with bypass recirculation, instead of a traditional enclosed aisle, where all the data center exhaust is forced to flow through the computer room air conditioners. The analysis of open-aisle data centers shows that as the temperature at the inlet of the racks becomes more nonuniform, optimal operation tends toward lower recirculation and higher power consumption; again, stressing the importance of providing as uniform a temperature to the racks as possible. It is also revealed that servers with a modest temperature rise (∼10°C) have a wider latitude for cooling infrastructure optimization than those with a high temperature rise (≥20°C), which tend to consume less cooling power when the aisles are enclosed.

Energy Modeling of Air-Cooled Data Centers: Part II—The Effect of Recirculation on the Energy Optimization of Open-Aisle, Air-Cooled Data Centers

2011 ◽  
Author(s):  
Dustin W. Demetriou ◽  
H. Ezzat Khalifa
Keyword(s):  
Power Consumption ◽  
Temperature Rise ◽  
Data Center ◽  
Data Centers ◽  
Energy Savings ◽  
Optimal Operation ◽  
Effect Of Temperature ◽  
Total Energy Consumption ◽  
Air Supply ◽  
Data Center Cooling

The work presented in this paper describes a simplified thermodynamic model that can be used for exploring optimization possibilities in air-cooled data centers. The model has been used to identify optimal, energy-efficient designs, operating scenarios, and operating parameters such as flow rates and air supply temperature. The model is used to parametrically evaluate the total energy consumption of the data center cooling infrastructure, by considering changes in the server temperature rise. The results of this parametric analysis highlight the important features that need to be considered when optimizing the operation of air-cooled data centers, especially the trade-off between low air supply temperature and increased air flow rate. The analysis is used to elucidate the deleterious effect of temperature non-uniformity at the inlet of the racks on the data center cooling infrastructure power consumption. A recirculation non-uniformity metric, θ, is introduced, which is the ratio of the maximum recirculation of any server to the average recirculation of all servers. The analysis of open-aisle data centers shows that as the recirculation non-uniformity at the inlet of the racks increases, optimal operation tends toward lower recirculation and higher power consumption; stressing the importance of providing as uniform conditions to the racks as possible. Cooling infrastructure energy savings greater than 40% are possible for a data center with uniform recirculation (θ = 0) compared to a data center with a typical recirculation non-uniformity (θ = 4). It is also revealed that servers with a modest temperature rise (∼10°C) have a wider latitude for cooling optimization than those with a high temperature rise (≥20°C).

Energy Modeling of Air-Cooled Data Centers: Part I—The Optimization of Enclosed Aisle Configurations

2011 ◽  
Author(s):  
Dustin W. Demetriou ◽  
H. Ezzat Khalifa
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Temperature Rise ◽  
Data Center ◽  
Data Centers ◽  
Total Power ◽  
Optimum Operating ◽  
Cooling Power ◽  
Inlet Temperature ◽  
Total Energy Consumption

The work presented in this paper describes a simplified thermodynamic model that can be used for exploring optimization possibilities in air-cooled data centers. The model is used to parametrically evaluate the total energy consumption of the data center cooling infrastructure for data centers that utilize aisle containment. 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. The analysis shows a potential for as much as a 57% savings in cooling infrastructure energy consumption by utilizing an optimized enclosed aisle configuration with bypass recirculation, instead of a traditional enclosed aisle, where all the data center exhaust is forced to flow through the computer room air conditioners (CRACs), for racks with a modest temperature rise (∼10°C). However, for racks with larger temperature rise (> ∼20°C), the saving are less than 5%. Furthermore, for servers whose fan speed (flow rate) varies as a function of inlet temperature, the analysis shows that the optimum operating regime for enclosed aisle data centers falls within a very narrow band and that power reductions are possible by lowering the uniform server inlet temperature in the enclosed aisle from 27°C to 22°C. However, the optimum CRAC exit temperature over the 22-to-27°C range of enclosed cold aisle temperature falls between ∼16 and 20°C because a significant reduction in the power consumption is possible through the use of bypass recirculation. Without bypass recirculation, the power consumption for a server inlet temperature of 22°C enclosed aisle case with a server temperature rise of 10°C would be a whopping 43% higher than with bypass recirculation. It is worth noting that, without bypass recirculation maintaining the enclosed cold aisle at 22°C instead of 27°C would reduce power consumption by 48%. It is also shown that enclosing the aisles together with bypass recirculation (when beneficial) also reduces the dependence of the optimum cooling power on server temperature rise.

Genetic algorithm based cooling energy optimization of data centers

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jayati Athavale ◽  
Minami Yoda ◽  
Yogendra Joshi
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Power Consumption ◽  
Data Center ◽  
Load Distribution ◽  
Data Centers ◽  
Cooling Power ◽  
Temperature Prediction ◽  
Content Type ◽  
Optimization Framework

Purpose This study aims to present development of genetic algorithm (GA)-based framework aimed at minimizing data center cooling energy consumption by optimizing the cooling set-points while ensuring that thermal management criteria are satisfied. Design/methodology/approach Three key components of the developed framework include an artificial neural network-based model for rapid temperature prediction (Athavale et al., 2018a, 2019), a thermodynamic model for cooling energy estimation and GA-based optimization process. The static optimization framework informs the IT load distribution and cooling set-points in the data center room to simultaneously minimize cooling power consumption while maximizing IT load. The dynamic framework aims to minimize cooling power consumption in the data center during operation by determining most energy-efficient set-points for the cooling infrastructure while preventing temperature overshoots. Findings Results from static optimization framework indicate that among the three levels (room, rack and row) of IT load distribution granularity, Rack-level distribution consumes the least cooling power. A test case of 7.5 h implementing dynamic optimization demonstrated a reduction in cooling energy consumption between 21%–50% depending on current operation of data center. Research limitations/implications The temperature prediction model used being data-driven, is specific to the lab configuration considered in this study and cannot be directly applied to other scenarios. However, the overall framework can be generalized. Practical implications The developed framework can be implemented in data centers to optimize operation of cooling infrastructure and reduce energy consumption. Originality/value This paper presents a holistic framework for improving energy efficiency of data centers which is of critical value given the high (and increasing) energy consumption by these facilities.

scholarly journals Outdoor Air-Cooling System for a Computer Room and Its Corresponding Energy-Saving Effect

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5719
Author(s):  
JiHyun Hwang ◽  
Taewon Lee
Keyword(s):  
Power Consumption ◽  
Energy Saving ◽  
Data Centers ◽  
Cooling System ◽  
Stable Operation ◽  
Air Cooling ◽  
Outdoor Air ◽  
Air Conditioners ◽  
Air Cooling System ◽  
Saving Effect

The recent expansion of the internet network and rapid advancements in information and communication technology are expected to lead to a significant increase in power consumption and the number of data centers. However, these data centers consume a considerable amount of electric power all year round, regardless of working days or holidays; thus, energy saving at these facilities has become essential. A disproportionate level of power consumption is concentrated in computer rooms because air conditioners in these rooms are required to operate throughout the year to maintain a constant indoor environment for stable operation of computer equipment with high-heat release densities. Considerable energy-saving potential is expected in such computer rooms, which consume high levels of energy, if an outdoor air-cooling system and air conditioners are installed. These systems can reduce the indoor space temperature by introducing a relatively low outdoor air temperature. Therefore, we studied the energy-saving effect of introducing an outdoor air-cooling system in a computer room with a disorganized arrangement of servers and an inadequate air conditioning system in a research complex in Korea. The findings of this study confirmed that annual energy savings of up to approximately 40% can be achieved.

scholarly journals Thermal Performance Evaluation of a Data Center Cooling System under Fault Conditions

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2996 ◽  
Author(s):  
Jinkyun Cho ◽  
Beungyong Park ◽  
Yongdae Jeong
Keyword(s):  
Water Supply ◽  
Thermal Performance ◽  
Data Center ◽  
Temperature Increase ◽  
Data Centers ◽  
Cooling System ◽  
Backup System ◽  
Air Supply ◽  
Chilled Water ◽  
Rapid Temperature

If a data center experiences a system outage or fault conditions, it becomes difficult to provide a stable and continuous information technology (IT) service. Therefore, it is critical to design and implement a backup system so that stability can be maintained even in emergency (unforeseen) situations. In this study, an actual 20 MW data center project was analyzed to evaluate the thermal performance of an IT server room during a cooling system outage under six fault conditions. In addition, a method of organizing and systematically managing operational stability and energy efficiency verification was identified for data center construction in accordance with the commissioning process. Up to a chilled water supply temperature of 17 °C and a computer room air handling unit air supply temperature of 24 °C, the temperature of the air flowing into the IT server room fell into the allowable range specified by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers standard (18–27 °C). It was possible to perform allowable operations for approximately 320 s after cooling system outage. Starting at a chilled water supply temperature of 18 °C and an air supply temperature of 25 °C, a rapid temperature increase occurred, which is a serious cause of IT equipment failure. Due to the use of cold aisle containment and designs with relatively high chilled water and air supply temperatures, there is a high possibility that a rapid temperature increase inside an IT server room will occur during a cooling system outage. Thus, the backup system must be activated within 300 s. It is essential to understand the operational characteristics of data centers and design optimal cooling systems to ensure the reliability of high-density data centers. In particular, it is necessary to consider these physical results and to perform an integrated review of the time required for emergency cooling equipment to operate as well as the backup system availability time.

Parametric Study of Hybrid Cooling Solution for Thermal Management of Data Centers

2007 ◽  
Author(s):  
Veerendra Mulay ◽  
Saket Karajgikar ◽  
Dereje Agonafer ◽  
Roger Schmidt ◽  
Madhusudan Iyengar
Keyword(s):  
Thermal Management ◽  
Data Center ◽  
Parametric Study ◽  
Heat Load ◽  
Data Centers ◽  
High Heat ◽  
Inlet Temperature ◽  
Air Cooling ◽  
Air Conditioners ◽  
Hybrid Cooling

The power trend for Server systems continues to grow thereby making thermal management of Data centers a very challenging task. Although various configurations exist, the raised floor plenum with Computer Room Air Conditioners (CRACs) providing cold air is a popular operating strategy. The air cooling of data center however, may not address the situation where more energy is expended in cooling infrastructure than the thermal load of data center. Revised power trend projections by ASHRAE TC 9.9 predict heat load as high as 5000W per square feet of compute servers’ equipment footprint by year 2010. These trend charts also indicate that heat load per product footprint has doubled for storage servers during 2000–2004. For the same period, heat load per product footprint for compute servers has tripled. Amongst the systems that are currently available and being shipped, many racks exceed 20kW. Such high heat loads have raised concerns over limits of air cooling of data centers similar to air cooling of microprocessors. A hybrid cooling strategy that incorporates liquid cooling along with air cooling can be very efficient in these situations. A parametric study of such solution is presented in this paper. A representative data center with 40 racks is modeled using commercially available CFD code. The variation in rack inlet temperature due to tile openings, underfloor plenum depths is reported.

scholarly journals Power consumption prediction in cloud data center using machine learning

2020 ◽  
Vol 10 (2) ◽  
pp. 1524 ◽  
Author(s):  
Deepika T. ◽  
Prakash P.
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Power Consumption ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Providers ◽  
Business World ◽  
Cloud Data ◽  
Cloud Data Centers

The flourishing development of the cloud computing paradigm provides several services in the industrial business world. Power consumption by cloud data centers is one of the crucial issues for service providers in the domain of cloud computing. Pursuant to the rapid technology enhancements in cloud environments and data centers augmentations, power utilization in data centers is expected to grow unabated. A diverse set of numerous connected devices, engaged with the ubiquitous cloud, results in unprecedented power utilization by the data centers, accompanied by increased carbon footprints. Nearly a million physical machines (PM) are running all over the data centers, along with (5 – 6) million virtual machines (VM). In the next five years, the power needs of this domain are expected to spiral up to 5% of global power production. The virtual machine power consumption reduction impacts the diminishing of the PM’s power, however further changing in power consumption of data center year by year, to aid the cloud vendors using prediction methods. The sudden fluctuation in power utilization will cause power outage in the cloud data centers. This paper aims to forecast the VM power consumption with the help of regressive predictive analysis, one of the Machine Learning (ML) techniques. The potency of this approach to make better predictions of future value, using Multi-layer Perceptron (MLP) regressor which provides 91% of accuracy during the prediction process.

Data Center Enhancement by Server Resources Utilization

2021 ◽  
Vol 39 (1B) ◽  
pp. 203-208
Author(s):  
Haider A. Ghanem ◽  
Rana F. Ghani ◽  
Maha J. Abbas
Keyword(s):  
Cloud Computing ◽  
Power Consumption ◽  
Resource Utilization ◽  
Virtual Machine ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
The Internet ◽  
Memory Resources

Data centers are the main nerve of the Internet because of its hosting, storage, cloud computing and other services. All these services require a lot of work and resources, such as energy and cooling. The main problem is how to improve the work of data centers through increased resource utilization by using virtual host simulations and exploiting all server resources. In this paper, we have considered memory resources, where Virtual machines were distributed to hosts after comparing the virtual machines with the host from where the memory and putting the virtual machine on the appropriate host, this will reduce the host machines in the data centers and this will improve the performance of the data centers, in terms of power consumption and the number of servers used and cost.

scholarly journals Open Source Monitoring System for IT Infrastructures Incorporating IoT-Based Sensors

Proceedings ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 56
Author(s):  
Alejandro Mosteiro Vázquez ◽  
Carlos Dafonte ◽  
Ángel Gómez
Keyword(s):  
Power Consumption ◽  
Open Source ◽  
Monitoring System ◽  
Data Center ◽  
Data Centers ◽  
Source Monitoring ◽  
State Of The Art ◽  
Memory Usage ◽  
Network Bandwidth ◽  
Status Data

This paper introduces the development of a data center monitoring system based on IoT technologies. The system is meant to work as an administrative tool for system administrators in any environment, but mainly focused on data centers, since it integrates sensor and server status data. We are developing a system that gives a broad view of a data center, integrating server data such as CPU and memory usage or network bandwidth with room health parameters such as temperature, humidity, and power consumption or the presence sensors that indicate if there were people inside the room at the time a certain event occurred. As this is a work in progress, in this paper, we present the state-of-the-art of this subject, as well as what we expect to obtain from this project.

Data Center Cooling Efficiency Improvement Through Localized and Optimized Cooling Resources Delivery

2012 ◽  
Author(s):  
Rongliang Zhou ◽  
Cullen Bash ◽  
Zhikui Wang ◽  
Alan McReynolds ◽  
Thomas Christian ◽  
...  
Keyword(s):  
Steady State ◽  
Data Center ◽  
Data Centers ◽  
Air Flow ◽  
Flow Distribution ◽  
Resource Provisioning ◽  
Cooling Power ◽  
Optimization Approach ◽  
Cooling Efficiency ◽  
Efficiency Improvement

Data centers are large computing facilities that can house tens of thousands of computer servers, storage and networking devices. They can consume megawatts of power and, as a result, reject megawatts of heat. For more than a decade, researchers have been investigating methods to improve the efficiency by which these facilities are cooled. One of the key challenges to maintain highly efficient cooling is to provide on demand cooling resources to each server rack, which may vary with time and rack location within the larger data center. In common practice today, chilled water or refrigerant cooled computer room air conditioning (CRAC) units are used to reject the waste heat outside the data center, and they also work together with the fans in the IT equipment to circulate air within the data center for heat transport. In a raised floor data center, the cool air exiting the multiple CRAC units enters the underfloor plenum before it is distributed through the vent tiles in the cold aisles to the IT equipment. The vent tiles usually have fixed openings and are not adapted to accommodate the flow demand that can vary from cold aisle to cold aisle or rack to rack. In this configuration, CRAC units have the extra responsibilities of cooling resources distribution as well as provisioning. The CRAC unit, however, does not have the fine control granularity to adjust air delivery to individual racks since it normally affects a larger thermal zone, which consists of a multiplicity of racks arranged into rows. To better match cool air demand on a per cold aisle or rack basis, floor-mounted adaptive vent tiles (AVT) can be used to replace CRAC units for air delivery adjustment. In this arrangement, each adaptive vent tile can be remotely commanded from fully open to fully close for finer local air flow regulation. The optimal configuration for a multitude of AVTs in a data center, however, can be far from intuitive because of the air flow complexity. To unleash the full potential of the AVTs for improved air flow distribution and hence higher cooling efficiency, we propose a two-step approach that involves both steady-state and dynamic optimization to optimize the cooling resource provisioning and distribution within raised-floor air cooled data centers with rigid or partial containment. We first perform a model-based steady-state optimization to optimize whole data center air flow distribution. Within each cold aisle, all AVTs are configured to a uniform opening setting, although AVT opening may vary from cold aisle to cold aisle. We then use decentralized dynamic controllers to optimize the settings of each CRAC unit such that the IT equipment thermal requirement is satisfied with the least cooling power. This two-step optimization approach simplifies the large scale dynamic control problem, and its effectiveness in cooling efficiency improvement is demonstrated through experiments in a research data center.

Sign in / Sign up

Export Citation Format

Share Document