The Prediction of Annual Energy Consumption by the Supply Air Temperature of Cooling Systems in Modular Data Centers

Author(s):  
Yong-Ho Jung
2017 ◽  
Vol 87 ◽  
pp. 131-146 ◽  
Author(s):  
Reza Farrahi Moghaddam ◽  
Vahid Asghari ◽  
Fereydoun Farrahi Moghaddam ◽  
Yves Lemieux ◽  
Mohamed Cheriet

2019 ◽  
Vol 123 ◽  
pp. 101729
Author(s):  
Yuanning Gao ◽  
Xiaofeng Gao ◽  
Yichen Zhu ◽  
Guihai Chen

Author(s):  
Yongmei Xu ◽  
Jingru Zhang ◽  
Yuhui Deng ◽  
Lan Du ◽  
Rong Jiao

Given the explosive growth of data, scalability and fault tolerance have become a fundamental challenge for data center network structures. Temperature in data centers significantly affects the failure ratio of high-speed network devices. Various types of air distribution schemes influence the temperature of network equipment differently, and the cooling cost in data centers dominates the overall energy cost. On the basis of the energy efficiency of cooling systems, this study analyzes and compares the thermal load distribution in the enclosure of standard and non-standard data centers by considering the effects of the external environment. Analysis results demonstrate that the external environment significantly affects the thermal load of non-standard data centers. By leveraging on the air temperature outside data centers and on the inlet/outlet of IT equipment, the air temperature and return air temperature of air conditioning are calculated when performing hot and cold aisle containment. The calculations indicate that sealing an appropriate aisle (hot or cold aisle) can significantly reduce the energy consumption of cooling systems in terms of the external air temperature outside data centers. Furthermore, if the air temperature outside data centers is higher than the temperature at the inlet of IT equipment, sealing the cold aisle outperforms sealing the hot aisle. By contrast, the aisle to be sealed depends on the energy efficiency ratio of the air conditioning.


Author(s):  
Tianyi Gao ◽  
Bahgat G. Sammakia ◽  
James Geer ◽  
Bruce Murray ◽  
Russell Tipton ◽  
...  

The heat dissipated by electronic equipment inside data centers is increasing at a rapid rate due to the increasing of performance requirement and package density. This ever increasing power leads to critical challenges of thermal management for these high power density data centers. Energy consumption is also a key issue for high density data centers. Roughly 1.5% of all U.S. electricity consumption in the year 2006 was related to data centers, while that number increased to 2% by the year 2010. In 2013, U.S. data centers consumed approximately 91 billion kilowatt-hours of electricity. This amount of the electricity equals the annual output of 34 500-megawatt coal-fired power plants [1]. Cooling systems constitute a significant portion of the energy consumption of data centers, being approximately 25%∼35% of the total energy usage. Therefore, there is a large potential to save energy by optimizing current existing cooling systems and investigating new cooling technologies, and, at the same time, improving the overall cooling capacity and efficiency. This paper describes and investigates a hybrid cooling technology which utilizes in row coolers in existing raised floor air cooled data centers. The in row cooler functions as a liquid-to-air heat exchanger. In addition to the traditional raised floor cold aisle-hot aisle arrangements, the in row cooler is installed between the IT equipment to enable delivering the liquid coolant medium closer to the IT equipment. The in row coolers intake the hot air from the hot aisle, condition it, and supply the chilled air to the cold aisle. Thus, by extracting a large portion of the heat more directly into the cooling liquid through the in row coolers compared with the perimeter CRAH unit, the overall cooling performance and efficiency can potentially be improved. CFD models for an in row cooler and a representative data center room are developed. Experimentally characterized performance data are used to calibrate and validate the models. The models are then used to conduct a detailed computational analysis to assess the effectiveness of different arrangement configurations of in row cooler units in two rows of racks along one cold aisle. The detailed performance of the entire cold aisle is characterized using the rack inlet air temperature and a temperature nonuniformity factor. The impact of CRAH location and room layout are also investigated. This study is based on a practical problem and the corresponding results and analysis provide basic installation and design guidelines for future equipment upgrading in certain parts of the data center.


Author(s):  
Betsegaw Gebrehiwot ◽  
Kushal Aurangabadkar ◽  
Naveen Kannan ◽  
Dereje Agonafer ◽  
Deepak Sivanandan ◽  
...  

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2382
Author(s):  
Kaixuan Ji ◽  
Ce Chi ◽  
Fa Zhang ◽  
Antonio Fernández Anta ◽  
Penglei Song ◽  
...  

The energy consumption problem has become a bottleneck hindering further development of data centers. However, the heterogeneity of servers, hybrid cooling modes, and extra energy caused by system state transitions increases the complexity of the energy optimization problem. To deal with such challenges, in this paper, an Energy Aware Task Scheduling strategy (EATS) utilizing marginal cost and task classification method is proposed that cooperatively improves the energy efficiency of servers and cooling systems. An energy consumption model for servers, cooling systems, and state transition is developed, and the energy optimization problem in data centers is formulated. The concept of marginal cost is introduced to guide the task scheduling process. The task classification method is incorporated with the idea of marginal cost to further improve resource utilization and reduce the total energy consumption of data centers. Experiments are conducted using real-world traces, and energy reduction results are compared. Results show that EATS achieves more energy-savings of servers, cooling systems, state transition in comparison to the other two techniques under a various number of servers, cooling modules and task arrival intensities. It is validated that EATS is effective at reducing total energy consumption and improving the resource utilization of data centers.


Author(s):  
Matteo Fiorani ◽  
Massimo Tornatore ◽  
Jiajia Chen ◽  
Lena Wosinska ◽  
Biswanath Mukherjee

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