scholarly journals Análise de Zonas Térmicas em Data Center Não-CRAC

2017 ◽  
Author(s):  
Ademir Camillo Junior ◽  
Charles C. Miers ◽  
Guilherme P. Koslovski ◽  
Mauricio A. Pillon
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Power Usage Effectiveness

A elevada concentração de equipamentos em Data Centers (DCs) é objeto de estudo para administradores, fabricantes (processadores, servidores e sistemas de refrigeração), entre outros. Dentre os desafios da área, destaca-se o melhoramento da eficiência energética destes ambientes. No contexto de DC, a Power Usage Effectiveness (PUE) é uma referência na mensuração da eficiência energética. Este trabalho apresenta a arquitetura MonTerDC, um sistema de monitoração de temperatura de DC baseado em sistemas de refrigeração não-CRAC. Como resultados, o trabalha apresenta o uso do MonTerDC na identificação de zonas térmicas indesejáveis (fora da norma). Com este mapeamento térmico em zonas, o administrador do DC pode aplicar práticas a` melhor distribuição física dos nós de computação e, consequentemente, reduzir a temperatura das zonas térmicas.

scholarly journals New Data Center Performance Index: Perfect Design Data Center—PDD

Climate ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 110
Author(s):  
Alexandre F. Santos ◽  
Pedro D. Gaspar ◽  
Heraldo J. L. de Souza
Keyword(s):  
Data Center ◽  
Air Conditioning ◽  
Data Centers ◽  
Current Method ◽  
Energy Usage ◽  
Design Data ◽  
The Difference ◽  
Power Usage Effectiveness ◽  
Approximate Result ◽  
Classification Table

Data Centers (DC) are specific buildings that require large infrastructures to store all the information needed by companies. All data transmitted over the network is stored on CDs. By the end of 2020, Data Centers will grow 53% worldwide. There are methodologies that measure the efficiency of energy consumption. The most used metric is the Power Usage Effectiveness (PUE) index, but it does not fully reflect efficiency. Three DC’s located at the cities of Curitiba, Londrina and Iguaçu Falls (Brazil) with close PUE values, are evaluated in this article using the Energy Usage Effectiveness Design (EUED) index as an alternative to the current method. EUED uses energy as a comparative element in the design phase. Infrastructure consumption is the sum of energy with Heating, Ventilating and Air conditioning (HVAC) equipment, equipment, lighting and others. The EUED values obtained were 1.245 (kWh/yr)/(kWh/yr), 1.313 (kWh/yr)/(kWh/yr) and 1.316 (kWh/yr)/(kWh/yr) to Curitiba, Londrina and Iguaçu Falls, respectively. The difference between the EUED and the PUE Constant External Air Temperature (COA) is 16.87% for Curitiba, 13.33% for Londrina and 13.30% for Iguaçu Falls. The new Perfect Design Data center (PDD) index prioritizes efficiency in increasing order is an easy index to interpret. It is a redefinition of EUED, given by a linear equation, which provides an approximate result and uses a classification table. It is a decision support index for the location of a Data Center in the project phase.

Technologies for the Energy-Efficient Data Center

2007 ◽  
Author(s):  
Tahir Cader ◽  
Levi Westra ◽  
Andres Marquez
Keyword(s):  
Pacific Northwest ◽  
Data Center ◽  
Data Centers ◽  
Cost Ratio ◽  
Typical Data ◽  
The Pacific ◽  
Power Usage Effectiveness ◽  
Four Levels ◽  
Broad Cross Section ◽  
The Impact

Although semiconductor manufacturers have provided temporary relief with lower-power multi-core microprocessors, OEMs and data center operators continue to push the limits for individual rack power densities. It is not uncommon today for data center operators to deploy multiple 20 kW racks in a facility. Such rack densities are exacerbating the major issues of power and cooling in data centers. Data center operators are now forced to take a hard look at the efficiencies of their data centers. Malone and Belady (2006) have proposed three metrics, i.e., Power Usage Effectiveness (PUE), Data Center Efficiency (DCE), and the Energy-to-Acquisition Cost ratio (EAC), to help data center operators quickly quantify the efficiency of their data centers. In their paper, Malone and Belady present nominal values of PUE across a broad cross-section of data centers. PUE values are presented for data centers at four levels of optimization. One of these optimizations involves the use of Computational Fluid Dynamics (CFD). In the current paper, CFD is used to conduct an in-depth investigation of a liquid-cooled data center that would potentially be housed at the Pacific Northwest National Labs (PNNL). The boundary conditions used in the CFD model are based upon actual measurements on a rack of liquid-cooled servers housed at PNNL. The analysis shows that the liquid-cooled facility could achieve a PUE of 1.57 as compared to a PUE of 3.0 for a typical data center (the lower the PUE, the better, with values below 1.6 approaching ideal). The increase in data center efficiency is also translated into an increase in the amount of IT equipment that can be deployed. At a PUE of 1.57, the analysis shows that 91% more IT equipment can be deployed as compared to the typical data center. The paper will discuss the analysis of the PUE, and will also explore the impact of the raising data center efficiency via the use of multiple cooling technologies and CFD analysis. Complete results of the analyses will be presented in the paper.

scholarly journals Capacity Management of Hyperscale Data Centers Using Predictive Modelling

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3438 ◽  
Author(s):  
Raihan Ul Islam ◽  
Xhesika Ruci ◽  
Mohammad Shahadat Hossain ◽  
Karl Andersson ◽  
Ah-Lian Kor
Keyword(s):  
Predictive Model ◽  
Data Center ◽  
Data Centers ◽  
Fuzzy Inference ◽  
Predictive Modelling ◽  
Machine Learning Techniques ◽  
Real World Data ◽  
Inference System ◽  
Power Usage Effectiveness ◽  
Artificial Neural Network Ann

Big Data applications have become increasingly popular with the emergence of cloud computing and the explosion of artificial intelligence. The increasing adoption of data-intensive machines and services is driving the need for more power to keep the data centers of the world running. It has become crucial for large IT companies to monitor the energy efficiency of their data-center facilities and to take actions on the optimization of these heavy electricity consumers. This paper proposes a Belief Rule-Based Expert System (BRBES)-based predictive model to predict the Power Usage Effectiveness (PUE) of a data center. The uniqueness of this model consists of the integration of a novel learning mechanism consisting of parameter and structure optimization by using BRBES-based adaptive Differential Evolution (BRBaDE), significantly improving the accuracy of PUE prediction. This model has been evaluated by using real-world data collected from a Facebook data center located in Luleå, Sweden. In addition, to prove the robustness of the predictive model, it has been compared with other machine learning techniques, such as an Artificial Neural Network (ANN) and an Adaptive Neuro Fuzzy Inference System (ANFIS), where it showed a better result. Further, due to the flexibility of the BRBES-based predictive model, it can be used to capture the nonlinear dependencies of many variables of a data center, allowing the prediction of PUE with much accuracy. Consequently, this plays an important role to make data centers more energy-efficient.

A Systems Overview of Commercial Data Centers

2019 ◽  
Vol 14 (1) ◽  
pp. 42-65 ◽  
Author(s):  
Sardar Khaliq Uzaman ◽  
Atta ur Rehman Khan ◽  
Junaid Shuja ◽  
Tahir Maqsood ◽  
Faisal Rehman ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Information And Communication Technologies ◽  
Data Center ◽  
Data Centers ◽  
Large Data ◽  
Vital Role ◽  
Research Issues ◽  
Computing Services ◽  
Power Usage Effectiveness

Data center facilities play a vital role in present and forthcoming information and communication technologies. Internet giants, such as IBM, Microsoft, Google, Yahoo, and Amazon hold large data centers to provide cloud computing services and web hosting applications. Due to rapid growth in data center size and complexity, it is essential to highlight important design aspects and challenges of data centers. This article presents market segmentation of the leading data center operators and discusses the infrastructural considerations, namely energy consumption, power usage effectiveness, cost structure, and system reliability constraints. Moreover, it presents data center network design, classification of the data center servers, recent developments, and future trends of the data center industry. Furthermore, the emerging paradigm of mobile cloud computing is debated with respect to the research issues. Preliminary results for the energy consumption of task scheduling techniques are also provided.

Identifying Constraints Regarding Power Usage Effectiveness for Data Centers Engaging in Demand Response Programs

2013 ◽  
Vol 284-287 ◽  
pp. 3597-3603
Author(s):  
Cheng Jen Tang ◽  
Miau Ru Dai
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Demand Response ◽  
Data Center ◽  
Data Centers ◽  
Green Building ◽  
Power Demand ◽  
Leed Certification ◽  
Improve Energy Efficiency ◽  
Power Usage Effectiveness

Demand response (DR) is an important ingredient and regarded as the killer application of the emerging smart grid. The continuously growing energy consumption of data centers makes data centers promising candidates with significant potential for DR. Participating in DR programs makes data centers have another finical resource in addition to service income. On the other hand, some government organizations also offer considerable incentives to promote energy saving actions for facilities with some certain certifications. Leadership in Energy and Environmental Design (LEED) rating system developed by U.S. Green Building Council (USGBC) is one of the most popular certification systems. LEED uses Power Usage Effectiveness (PUE) as one of the metrics for quantifying how energy efficient a data center is. The goal of PUE is to improve energy efficiency of a data center. DR programs require participants to temporarily reduce their power demand on some occasions with little concern regarding energy efficiency. To enjoy incentives from LEED certification, data center administrators need to know whether the participation of DR hampers the established PUE of their facilities or not. This paper examines the power consumption models from prior studies, and identifies the constraints introduced by PUE for data centers participating in DR programs. The examination reveals that the ratios of static power consumption to the dynamic power demand range of different types of data center equipment do affect PUE while taking demand reduction efforts. With this finding, facility managers of data centers have a clear picture of what to expect from the DR participation, and what to adjust of their data center equipment.

Data Center Equipment Reliability Concerns—Contamination Issues, Standards Actions, and Case Studies

2013 ◽  
Author(s):  
Chris Muller ◽  
Chuck Arent ◽  
Henry Yu
Keyword(s):  
Case Studies ◽  
Data Center ◽  
Data Centers ◽  
Electronic Equipment ◽  
Circuit Board ◽  
Lead Free ◽  
Reliable Operation ◽  
Contamination Assessment ◽  
Equipment Reliability ◽  
Equipment Failures

Abstract Lead-free manufacturing regulations, reduction in circuit board feature sizes and the miniaturization of components to improve hardware performance have combined to make data center IT equipment more prone to attack by corrosive contaminants. Manufacturers are under pressure to control contamination in the data center environment and maintaining acceptable limits is now critical to the continued reliable operation of datacom and IT equipment. This paper will discuss ongoing reliability issues with electronic equipment in data centers and will present updates on ongoing contamination concerns, standards activities, and case studies from several different locations illustrating the successful application of contamination assessment, control, and monitoring programs to eliminate electronic equipment failures.

scholarly journals Is This a Geolibrary? A Case of the Idaho Geospatial Data Center

2017 ◽  
Vol 19 (1) ◽  
pp. 4-10 ◽  
Author(s):  
Maria Anna Jankowska ◽  
Piotr Jankowski
Keyword(s):  
Digital Library ◽  
Future Development ◽  
Data Center ◽  
Data Centers ◽  
Public Domain ◽  
The State ◽  
Geospatial Data ◽  
Design And Implementation ◽  
Geographic Data

The article presents the Idaho Geospatial Data Center (IGDC), a digital library of public-domain geographic data for the state of Idaho. The design and implementation of IGDC are introduced as part of the larger context of a geolibrary model. The article presents methodology and tools used to build IGDC with the focus on a geolibrary map browser. The use of IGDC is evaluated from the perspective of accessa and demand for geographic data. Finally, the article offers recommendations for future development of geospatial data centers.

Perimeter Cooling Unit and Localized Row-Based Cooling Unit Transient Air Flow Effects Modeling and Characterization in Data Centers

2015 ◽  
Author(s):  
Tianyi Gao ◽  
James Geer ◽  
Bahgat G. Sammakia ◽  
Russell Tipton ◽  
Mark Seymour
Keyword(s):  
Thermal Management ◽  
Data Center ◽  
Data Centers ◽  
Cooling System ◽  
Air Flow ◽  
Cooling Systems ◽  
Dynamic Thermal Management ◽  
Hybrid Cooling ◽  
Cooling Unit ◽  
Cooling Air

Cooling power constitutes a large portion of the total electrical power consumption in data centers. Approximately 25%∼40% of the electricity used within a production data center is consumed by the cooling system. Improving the cooling energy efficiency has attracted a great deal of research attention. Many strategies have been proposed for cutting the data center energy costs. One of the effective strategies for increasing the cooling efficiency is using dynamic thermal management. Another effective strategy is placing cooling devices (heat exchangers) closer to the source of heat. This is the basic design principle of many hybrid cooling systems and liquid cooling systems for data centers. Dynamic thermal management of data centers is a huge challenge, due to the fact that data centers are operated under complex dynamic conditions, even during normal operating conditions. In addition, hybrid cooling systems for data centers introduce additional localized cooling devices, such as in row cooling units and overhead coolers, which significantly increase the complexity of dynamic thermal management. Therefore, it is of paramount importance to characterize the dynamic responses of data centers under variations from different cooling units, such as cooling air flow rate variations. In this study, a detailed computational analysis of an in row cooler based hybrid cooled data center is conducted using a commercially available computational fluid dynamics (CFD) code. A representative CFD model for a raised floor data center with cold aisle-hot aisle arrangement fashion is developed. The hybrid cooling system is designed using perimeter CRAH units and localized in row cooling units. The CRAH unit supplies centralized cooling air to the under floor plenum, and the cooling air enters the cold aisle through perforated tiles. The in row cooling unit is located on the raised floor between the server racks. It supplies the cooling air directly to the cold aisle, and intakes hot air from the back of the racks (hot aisle). Therefore, two different cooling air sources are supplied to the cold aisle, but the ways they are delivered to the cold aisle are different. Several modeling cases are designed to study the transient effects of variations in the flow rates of the two cooling air sources. The server power and the cooling air flow variation combination scenarios are also modeled and studied. The detailed impacts of each modeling case on the rack inlet air temperature and cold aisle air flow distribution are studied. The results presented in this work provide an understanding of the effects of air flow variations on the thermal performance of data centers. The results and corresponding analysis is used for improving the running efficiency of this type of raised floor hybrid data centers using CRAH and IRC units.

Exergy Analysis of Data Center Thermal Management Systems

2003 ◽  
Author(s):  
Amip J. Shah ◽  
Van P. Carey ◽  
Cullen E. Bash ◽  
Chandrakant D. Patel
Keyword(s):  
Thermal Management ◽  
Data Center ◽  
Data Centers ◽  
Exergy Analysis ◽  
Thermal Control ◽  
Management Systems ◽  
World Systems ◽  
Sample Data ◽  
Potential Applications ◽  
Temperature Ranges

Data centers today contain more computing and networking equipment than ever before. As a result, a higher amount of cooling is required to maintain facilities within operable temperature ranges. Increasing amounts of resources are spent to achieve thermal control, and tremendous potential benefit lies in the optimization of the cooling process. This paper describes a study performed on data center thermal management systems using the thermodynamic concept of exergy. Specifically, an exergy analysis has been performed on sample data centers in an attempt to identify local and overall inefficiencies within thermal management systems. The development of a model using finite volume analysis has been described, and potential applications to real-world systems have been illustrated. Preliminary results suggest that such an exergy-based analysis can be a useful tool in the design and enhancement of thermal management systems.

Numerical Study to Analyze the Effects of Hot/Cold Aisle Configurations on Heat Transfer Phenomenon in a Data Center

2013 ◽  
Author(s):  
Kamran Nazir ◽  
Naveed Durrani ◽  
Imran Akhtar ◽  
M. Saif Ullah Khalid
Keyword(s):  
Heat Transfer ◽  
Data Center ◽  
Data Centers ◽  
Cooling System ◽  
Numerical Study ◽  
High Energy ◽  
Research Area ◽  
Transfer Phenomenon ◽  
Heat Transfer Phenomenon ◽  
Active Research

Due to high energy demands of data centers and the energy crisis throughout the world, efficient heat transfer in a data center is an active research area. Until now major emphasis lies upon study of air flow rate and temperature profiles for different rack configurations and tile layouts. In current work, we consider different hot aisle (HA) and cold aisle (CA) configurations to study heat transfer phenomenon inside a data center. In raised floor data centers when rows of racks are parallel to each other, in a conventional cooling system, there are equal number of hot and cold aisles for odd number of rows of racks. For even number of rows of racks, whatever configuration of hot/cold aisles is adopted, number of cold aisles is either one greater or one less than number of hot aisles i.e. two cases are possible case A: n(CA) = n(HA) + 1 and case B: n(CA) = n(HA) − 1 where n(CA), n(HA) denotes number of cold and hot aisles respectively. We perform numerical simulations for two (case1) and four (case 2) racks data center. The assumption of constant pressure below plenum reduces the problem domain to above plenum area only. In order to see which configuration provides higher heat transfer across servers, we measure heat transfer across servers on the basis of temperature differences across racks, and in order to validate them, we find mass flow rates on rack outlet. On the basis of results obtained, we conclude that for even numbered rows of rack data center, using more cold aisles than hot aisles provide higher heat transfer across servers. These results provide guidance on the design and layout of a data center.

Sign in / Sign up

Export Citation Format

Share Document