Evaluation Metrics of Thermal Management in Data Centers Based on Energy Efficiency Measurement Assessment for Cooling Systems

2018 ◽  
Vol 30 (12) ◽  
pp. 620-630 ◽  
Author(s):  
Jinkyun Cho ◽  
Beungyong Park ◽  
Yongdae Jeong
Keyword(s):  
Energy Efficiency ◽  
Thermal Management ◽  
Data Centers ◽  
Efficiency Measurement ◽  
Evaluation Metrics ◽  
Cooling Systems

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.

Energy Aware Grid: Global Workload Placement Based on Energy Efficiency

2003 ◽  
Author(s):  
Chandrakant Patel ◽  
Ratnesh Sharma ◽  
Cullen Bash ◽  
Sven Graupner
Keyword(s):  
Energy Efficiency ◽  
Thermal Management ◽  
Pervasive Computing ◽  
Data Center ◽  
Data Centers ◽  
Global Network ◽  
New Delhi ◽  
Energy Aware ◽  
And Storage ◽  
Grid Based

Computing will be pervasive, and enablers of pervasive computing will be data centers housing computing, networking and storage hardware. The data center of tomorrow is envisaged as one containing thousands of single board computing systems deployed in racks. A data center, with 1000 racks, over 30,000 square feet, would require 10 MW of power to power the computing infrastructure. At this power dissipation, an additional 5 MW would be needed by the cooling resources to remove the dissipated heat. At $100/MWh, the cooling alone would cost $4 million per annum for such a data center. The concept of Computing Grid, based on coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations, is emerging as the new paradigm in distributed and pervasive computing for scientific as well as commercial applications. We envision a global network of data centers housing an aggregation of computing, networking and storage hardware. The increased compaction of such devices in data centers has created thermal and energy management issues that inhibit sustainability of such a global infrastructure. In this paper, we propose the framework of Energy Aware Grid that will provide a global utility infrastructure explicitly incorporating energy efficiency and thermal management among data centers. Designed around an energy-aware co-allocator, workload placement decisions will be made across the Grid, based on data center energy efficiency coefficients. The coefficient, evaluated by the data center’s resource allocation manager, is a complex function of the data center thermal management infrastructure and the seasonal and diurnal variations. A detailed procedure for implementation of a test case is provided with an estimate of energy savings to justify the economics. An example workload deployment shown in the paper aspires to seek the most energy efficient data center in the global network of data centers. The locality based energy efficiency in a data center is shown to arise from use of ground coupled loops in cold climates to lower ambient temperature for heat rejection e.g. computing and rejecting heat from a data center at nighttime ambient of 20°C. in New Delhi, India while Phoenix, USA is at 45°C. The efficiency in the cooling system in the data center in New Delhi is derived based on lower lift from evaporator to condenser. Besides the obvious advantage due to external ambient, the paper also incorporates techniques that rate the efficiency arising from internal thermo-fluids behavior of a data center in workload placement decision.

scholarly journals Improvement of Energy Efficiency and Control Performance of Cooling System Fan Applied to Industry 4.0 Data Center

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 582 ◽  
Author(s):  
Jae-Sub Ko ◽  
Jun-Ho Huh ◽  
Jong-Chan Kim
Keyword(s):  
Energy Efficiency ◽  
Industry 4.0 ◽  
Data Centers ◽  
Fuzzy Controller ◽  
Cooling System ◽  
Pi Controller ◽  
Control Performance ◽  
Cooling Systems ◽  
Proportional Integral ◽  
And Performance

This paper proposes a control method to improve the energy efficiency and performance of cooling fans used for cooling. In Industry 4.0, a large number of digital data are used, and a large number of data centers are created to handle these data. These data centers consist of information technology (IT) equipment, power systems, and cooling systems. The cooling system is essential to prevent failure and malfunction of the IT equipment, which consumes a considerable amount of energy. This paper proposes a method to reduce the energy used in such cooling systems and to improve the temperature control performance. This paper proposes an fuzzy proportional integral(FPI) controller that controls the input value of the proportional integral(PI) controller by the fuzzy controller according to the operation state, a VFPI (Variable Fuzzy Proportional Integral) controller that adjusts the gain value of the fuzzy controller, and a variable fuzzy proportion integration-variable limit (VFPI-VL) controller that adjusts the limit value of the fuzzy controller’s output value. These controllers control the fan applied to the cooling system and compare the energy consumed and temperature control performance. When the PI controller consumes 100% of the power consumed, the FPI is 50.5%, the VFPI controller is 44.3%, and the VFPI-VL is 32.6%. The power consumption is greatly reduced. In addition, the VFPI-VL controller is the lowest in temperature variation, which improves the energy efficiency and performance of the cooling system using a fan. The methods presented in this paper can not only be applied to fans for cooling, but also to variable speed systems for various purposes and improvement of performance and efficiency can be expected.

The Environmental Footprint of Data Centers

2009 ◽  
Author(s):  
Amip Shah ◽  
Cullen Bash ◽  
Ratnesh Sharma ◽  
Tom Christian ◽  
Brian J. Watson ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Data Center ◽  
Energy Use ◽  
Data Centers ◽  
Evaluation Metrics ◽  
Environmental Footprint ◽  
Diverse Range ◽  
Use Of Data ◽  
Operational Energy

Numerous evaluation metrics and standards are being proposed across industry and government to measure and monitor the energy efficiency of data centers. However, the energy use of data centers is just one aspect of the environmental impact. In this paper, we explore the overall environmental footprint of data centers beyond just energy efficiency. Building upon established procedures from the environmental sciences, we create an end-to-end life-cycle model of the environmental footprint of data centers across a diverse range of impacts. We test this model in the case study of a hypothetical 2.2-MW data center. Our analysis suggests the need for evaluation metrics that go beyond just operational energy use in order to achieve sustainable data centers.

Analyzing the Cooling Efficiency of the Thermal Environment in Data Centers

2014 ◽  
Vol 6 (4) ◽  
pp. 15-27
Author(s):  
Yongmei Xu ◽  
Jingru Zhang ◽  
Yuhui Deng ◽  
Lan Du ◽  
Rong Jiao
Keyword(s):  
Energy Efficiency ◽  
Air Temperature ◽  
Thermal Load ◽  
Air Conditioning ◽  
High Speed ◽  
Data Centers ◽  
Thermal Environment ◽  
External Environment ◽  
Cooling Systems ◽  
Standard Data

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.

Evaluating Life-Cycle Environmental Impact of Data Centers

2011 ◽  
Vol 133 (3) ◽  
Author(s):  
Amip Shah ◽  
Cullen Bash ◽  
Ratnesh Sharma ◽  
Tom Christian ◽  
Brian J. Watson ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Life Cycle ◽  
Environmental Impact ◽  
Energy Use ◽  
Data Centers ◽  
Evaluation Metrics ◽  
Environmental Footprint ◽  
Diverse Range ◽  
Use Of Data ◽  
Operational Energy

Numerous evaluation metrics and standards are being proposed across industry and government to measure and monitor the energy efficiency of data centers. However, the energy use of data centers is just one aspect of the environmental impact. In this paper, we explore the overall environmental footprint of data centers beyond just energy efficiency. Building upon established procedures from the environmental sciences, we create an end-to-end life-cycle model of the environmental footprint of data centers across a diverse range of impacts. We test this model in the case study of a hypothetical 2.2-MW data center. Our analysis suggests the need for evaluation metrics that go beyond just operational energy use in order to achieve sustainable data centers.

scholarly journals Cooperatively Improving Data Center Energy Efficiency Based on Multi-Agent Deep Reinforcement Learning

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2071
Author(s):  
Ce Chi ◽  
Kaixuan Ji ◽  
Penglei Song ◽  
Avinab Marahatta ◽  
Shikui Zhang ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Reinforcement Learning ◽  
Resource Utilization ◽  
Data Center ◽  
Data Centers ◽  
Optimization Method ◽  
Comparison Method ◽  
Cooling Systems ◽  
Model Free ◽  
Multi Agent

The problem of high power consumption in data centers is becoming more and more prominent. In order to improve the energy efficiency of data centers, cooperatively optimizing the energy of IT systems and cooling systems has become an effective way. In this paper, a model-free deep reinforcement learning (DRL)-based joint optimization method MAD3C is developed to overcome the high-dimensional state and action space problems of the data center energy optimization. A hybrid AC-DDPG cooperative multi-agent framework is devised for the improvement of the cooperation between the IT and cooling systems for further energy efficiency improvement. In the framework, a scheduling baseline comparison method is presented to enhance the stability of the framework. Meanwhile, an adaptive score is designed for the architecture in consideration of multi-dimensional resources and resource utilization improvement. Experiments show that our proposed approach can effectively reduce energy for data centers through the cooperative optimization while guaranteeing training stability and improving resource utilization.

Sign in / Sign up

Export Citation Format

Share Document