Optimization of Thermoelectric Modules for Computer System Thermal Management and Infrastructure Energy Efficiency

2008 ◽  
Author(s):  
Amip J. Shah ◽  
Anita Rogacs ◽  
Chandrakant D. Patel
Keyword(s):  
Energy Efficiency ◽  
Power Systems ◽  
Thermal Management ◽  
Data Centers ◽  
High Density ◽  
Coefficient Of Performance ◽  
System Level ◽  
Ongoing Research ◽  
Density Data ◽  
Cost Of Ownership

With the proliferation of multiple cores on a single processor and the integration of numerous stacked die in chip packages, the resulting non-uniformity in power dissipation necessitates spatially and temporally localized control of temperature in the package. Thermoelectric (TE) devices can potentially provide one mechanism to achieve such control. Unfortunately, at typical junction-to-ambient temperatures, the coefficient-of-performance (COP) of existing bulk TE devices tends to be quite low. As a result, for many high-power systems, the additional power input required to operate a TE cooling module can lead to an increase in the overall system cost-of-ownership, causing TE cooling solutions to be excluded from cost-sensitive thermal management solutions in high-volume computer systems. However, recent trends of compaction and consolidation of computer servers in high-density data centers have resulted in a dramatic increase in the burdened cost-of-ownership of mission-critical IT facilities. For example, the energy consumption of the cooling infrastructure for many high-density data centers can equal or exceed the power consumption of the compute equipment. Thus, for the growing enterprise thermal management segment, the appropriate metric is no longer the COP of the thermal solution but rather the COP of the cooling ensemble, which takes into account the energy efficiency of cooling solutions in the chip, system, rack, data center and facility. To examine the effects of chip-level COP on the ensemble-level COP, this paper explores a case study comparing two ensemble solutions. In one case, local hotspots on a chip in a three-dimensional package are mitigated by increasing fan power at the system level (and subsequently, in the computer room and the rest of the ensemble). In another case, local hotspots at the chip are mitigated through spot-cooling via TE cooling modules. For each of these cases, the COP of the ensemble is evaluated. The model suggests that while feasible, the benefit of using TEs at current performance levels is limited. However, ongoing research that may improve TE performance in the future has the potential to enhance infrastructure energy efficiency.

Influence of Experimental Uncertainty on Prediction of Holistic Multi-Scale Data Center Energy Efficiency

2011 ◽  
Author(s):  
Thomas J. Breen ◽  
Ed J. Walsh ◽  
Jeff Punch ◽  
Amip J. Shah ◽  
Niru Kumari ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Experimental Analysis ◽  
Data Center ◽  
Data Centers ◽  
Operating Conditions ◽  
Coefficient Of Performance ◽  
System Level ◽  
Scale Model ◽  
Multi Scale ◽  
Scale Data

As the energy footprint of data centers continues to increase, models that allow for “what-if” simulations of different data center design and management paradigms will be important. Prior work by the authors has described a multi-scale energy efficiency model that allows for evaluating the coefficient of performance of the data center ensemble (COPGrand), and demonstrated the utility of such a model for purposes of choosing operational set-points and evaluating design trade-offs. However, experimental validation of these models poses a challenge because of the complexity involved with tailoring such a model for implementation to legacy data centers, with shared infrastructure and limited control over IT workload. Further, test facilities with dummy heat loads or artificial racks in lieu of IT equipment generally have limited utility in validating end-to-end models owing to the inability of such loads to mimic phenomena such as fan scalability, etc. In this work, we describe the experimental analysis conducted in a special test chamber and data center facility. The chamber, focusing on system level effects, is loaded with an actual IT rack, and a compressor delivers chilled air to the chamber at a preset temperature. By varying the load in the IT rack as well as the air delivery parameters — such as flow rate, supply temperature, etc. — a setup which simulates the system level of a data center is created. Experimental tests within a live data center facility are also conducted where the operating conditions of the cooling infrastructure are monitored — such as fluid temperatures, flow rates, etc. — and can be analyzed to determine effects such as air flow recirculation, heat exchanger performance, etc. Using the experimental data a multi-scale model configuration emulating the data center can be defined. We compare the results from such experimental analysis to a multi-scale energy efficiency model of the data center, and discuss the accuracies as well as inaccuracies within such a model. Difficulties encountered in the experimental work are discussed. The paper concludes by discussing areas for improvement in such modeling and experimental evaluation. Further validation of the complete multi-scale data center energy model is planned.

Application of Exploratory Data Analysis (EDA) Techniques to Temperature Data in a Conventional Data Center

2007 ◽  
Author(s):  
Ratnesh Sharma ◽  
Rocky Shih ◽  
Chandrakant Patel ◽  
John Sontag
Keyword(s):  
Data Analysis ◽  
Thermal Management ◽  
High Power ◽  
Data Center ◽  
Data Centers ◽  
Exploratory Data Analysis ◽  
Temperature Data ◽  
High Density ◽  
Exploratory Data ◽  
Mission Critical

Data centers are the computational hub of the next generation. Rise in demand for computing has driven the emergence of high density datacenters. With the advent of high density, mission-critical datacenters, demand for electrical power for compute and cooling has grown. Deployment of a large number of high powered computer systems in very dense configurations in racks within data centers will result in very high power densities at room level. Hosting business and mission-critical applications also demand a high degree of reliability and flexibility. Managing such high power levels in the data center with cost effective reliable cooling solutions is essential to feasibility of pervasive compute infrastructure. Energy consumption of data centers can also be severely increased by over-designed air handling systems and rack layouts that allow the hot and cold air streams to mix. Absence of rack level temperature monitoring has contributed to lack of knowledge of air flow patterns and thermal management issues in conventional data centers. In this paper, we present results from exploratory data analysis (EDA) of rack-level temperature data collected over a period of several months from a conventional production datacenter. Typical datacenters experience surges in power consumption due to rise and fall in compute demand. These surges can be long term, short term or periodic, leading to associated thermal management challenges. Some variations may also be machine-dependent and vary across the datacenter. Yet other thermal perturbations may be localized and momentary. Random variations due to sensor response and calibration, if not identified, may lead to erroneous conclusions and expensive faults. Among other indicators, EDA techniques also reveal relationships among sensors and deployed hardware in space and time. Identification of such patterns can provide significant insight into data center dynamics for future forecasting purposes. Knowledge of such metrics enables energy-efficient thermal management by helping to create strategies for normal operation and disaster recovery for use with techniques like dynamic smart cooling.

scholarly journals A Comparative CFD Study of Two Air Distribution Systems with Hot Aisle Containment in High-Density Data Centers

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6147
Author(s):  
Jinkyun Cho ◽  
Jesang Woo ◽  
Beungyong Park ◽  
Taesub Lim
Keyword(s):  
Thermal Performance ◽  
Data Center ◽  
Distribution Systems ◽  
Data Centers ◽  
High Density ◽  
Operating Conditions ◽  
Air Distribution ◽  
Density Data ◽  
Computational Fluid Dynamics Analysis ◽  
Hard Floor

Removing heat from high-density information technology (IT) equipment is essential for data centers. Maintaining the proper operating environment for IT equipment can be expensive. Rising energy cost and energy consumption has prompted data centers to consider hot aisle and cold aisle containment strategies, which can improve the energy efficiency and maintain the recommended level of inlet air temperature to IT equipment. It can also resolve hot spots in traditional uncontained data centers to some degree. This study analyzes the IT environment of the hot aisle containment (HAC) system, which has been considered an essential solution for high-density data centers. The thermal performance was analyzed for an IT server room with HAC in a reference data center. Computational fluid dynamics analysis was conducted to compare the operating performances of the cooling air distribution systems applied to the raised and hard floors and to examine the difference in the IT environment between the server rooms. Regarding operating conditions, the thermal performances in a state wherein the cooling system operated normally and another wherein one unit had failed were compared. The thermal performance of each alternative was evaluated by comparing the temperature distribution, airflow distribution, inlet air temperatures of the server racks, and recirculation ratio from the outlet to the inlet. In conclusion, the HAC system with a raised floor has higher cooling efficiency than that with a hard floor. The HAC with a raised floor over a hard floor can improve the air distribution efficiency by 28%. This corresponds to 40% reduction in the recirculation ratio for more than 20% of the normal cooling conditions. The main contribution of this paper is that it realistically implements the effectiveness of the existing theoretical comparison of the HAC system by developing an accurate numerical model of a data center with a high-density fifth-generation (5G) environment and applying the operating conditions.

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.

Energy-Efficient Air Cooling of Data Centers at 2000 W/ft2

2009 ◽  
Author(s):  
Magnus K. Herrlin ◽  
Michael K. Patterson
Keyword(s):  
Energy Efficiency ◽  
Data Center ◽  
Energy Efficient ◽  
Data Centers ◽  
High Heat ◽  
High Density ◽  
Cfd Modeling ◽  
Air Cooling ◽  
Capital Costs ◽  
Design Considerations

Increased Information and Communications Technology (ICT) capability and improved energy-efficiency of today’s server platforms have created opportunities for the data center operator. However, these platforms also test the ability of many data center cooling systems. New design considerations are necessary to effectively cool high-density data centers. Challenges exist in both capital costs and operational costs in the thermal management of ICT equipment. This paper details how air cooling can be used to address both challenges to provide a low Total Cost of Ownership (TCO) and a highly energy-efficient design at high heat densities. We consider trends in heat generation from servers and how the resulting densities can be effectively cooled. A number of key factors are reviewed and appropriate design considerations developed to air cool 2000 W/ft2 (21,500 W/m2). Although there are requirements for greater engineering, such data centers can be built with current technology, hardware, and best practices. The density limitations are shown primarily from an airflow management and cooling system controls perspective. Computational Fluid Dynamics (CFD) modeling is discussed as a key part of the analysis allowing high-density designs to be successfully implemented. Well-engineered airflow management systems and control systems designed to minimize airflow by preventing mixing of cold and hot airflows allow high heat densities. Energy efficiency is gained by treating the whole equipment room as part of the airflow management strategy, making use of the extended environmental ranges now recommended and implementing air-side air economizers.

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