scholarly journals Assessment of Emerging Cooling Technologies by Analyzing Their Impact on Reducing the Power Usage Effectiveness Ratio of Data Centers

2019 ◽  
Author(s):  
Omkar Gadgil ◽  
Keith A. Rahn
Keyword(s):  
Data Centers ◽  
Power Usage Effectiveness

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.

Power usage effectiveness in data centers: overloaded and underachieving

2016 ◽  
Vol 29 (4) ◽  
pp. 61-69 ◽  
Author(s):  
Nathaniel Horner ◽  
Inês Azevedo
Keyword(s):  
Data Centers ◽  
Power Usage Effectiveness

scholarly journals Analysis of methods for assessing the energy efficiency of data centers using the power usage effectiveness method

2022 ◽  
Vol 1216 (1) ◽  
pp. 012014
Author(s):  
R Uanov ◽  
A S Begimbetova
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Data Centers ◽  
Cooling System ◽  
Assessment Method ◽  
Vapor Compression ◽  
Compression Cycle ◽  
Free Cooling ◽  
Power Usage Effectiveness ◽  
Machine Room

Abstract The article deals with the analysis of methods for assessing the energy efficiency of data centers according to the Power Usage Effectiveness method. The demand for data centers which consumes a large amount of electricity is growing with the growth of digitalization and the accumulation of big data in the network. The energy consumption of the cooling system for the machine room accounts for a significant part of the operating costs of the building. Free cooling in a refrigeration system reduces energy consumption much more than operating systems with a vapor-compression cycle. In 2006 according to The Green Grid, the assessment method of Power Usage Effectiveness has become an international standard for measuring energy efficiency and is widely used in the design and operation of data centers. In this regard, the operation principles of free-cooling chillers are considered. The calculation example of the system payback in free-cooling is also given.

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 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 Power Usage Effectiveness Metrics to Measure Efficiency and Performance of Data Centers

2014 ◽  
Vol 8 (5) ◽  
pp. 2207-2216 ◽  
Author(s):  
Mueen Uddin ◽  
Raed Alsaqour ◽  
Asadullah Shah ◽  
Tanzila Saba
Keyword(s):  
Data Centers ◽  
Power Usage Effectiveness ◽  
And Performance

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.

Case Study for Energy-Efficiency of Liquid-Cooled Data Centers

2011 ◽  
Author(s):  
Ricardo Rivera-Lopez ◽  
Mark Kimber
Keyword(s):  
Energy Efficiency ◽  
Data Centers ◽  
Energy Savings ◽  
Liquid Cooling ◽  
Typical Data ◽  
Thermal Solution ◽  
Multiple Measurements ◽  
Power Usage Effectiveness ◽  
Computer Board

The thermal management of existing data centers is centered on forced convection using air as the transport fluid. A large portion of the energy required for typical data centers is used in maintaining reasonable operating temperatures, and many have looked to liquid cooling as a promising solution to increased energy efficiency. The current work is a case study of making this transition for a single computer board. The energy savings potential is quantified and the removal of heat via liquid cooling is characterized from the chip level to the environment. A thermal solution model is developed and validated through experimentation. The experiment consists of a rack-mounted computer board to simulate a server and cold plates attached at several key locations for cooling. Multiple measurements are made to determine the amount of heat removed and power consumed in the process. The results from this study show that liquid-cooling presents an improved thermal solution to data centers and the energy savings potential is large, which improves the power usage effectiveness since power is mostly used in data processing rather than server cooling.

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