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.

An Exergy-Based Control Strategy for Computer Room Air-Conditioning Units in Data Centers

2004 ◽  
Author(s):  
Amip J. Shah ◽  
Van P. Carey ◽  
Cullen E. Bash ◽  
Chandrakant D. Patel
Keyword(s):  
Power Consumption ◽  
Thermal Management ◽  
Control Strategy ◽  
Data Center ◽  
Air Conditioning ◽  
Data Centers ◽  
Storage Systems ◽  
Efficient Data ◽  
And Storage ◽  
Good Agreement

Recent compaction and miniaturization of electronic equipment has caused a dramatic increase in the amount of heat dissipated within data centers housing compute, network, and storage systems. The efficient thermal management of these systems is complicated by the intricate interdependence among the various components of the thermal architecture, including the heat-dissipating computer racks, the Computer Room Air-Conditioning (CRAC) units, and the physical airspace within the room. To account for this interdependence, an approach based on the thermodynamic metric of exergy has been proposed, which allows for prediction of an optimal CRAC operating point that corresponds to the point of minimal irreversibility for the overall system. To validate the formulated theory, predictions from the model have been compared with actual data center power consumption measurements. Initial comparisons indicate good agreement, suggesting that the proposed theory has great applicability for efficient data center thermal management.

scholarly journals Productive Efficiency of Energy-Aware Data Centers

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2053 ◽  
Author(s):  
Damián Fernández-Cerero ◽  
Alejandro Fernández-Montes ◽  
Francisco Velasco
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Data Center ◽  
Information Technologies ◽  
Data Centers ◽  
Large Data ◽  
Productive Efficiency ◽  
Energy Policies ◽  
Energy Aware ◽  
Constant Returns To Scale

Information technologies must be made aware of the sustainability of cost reduction. Data centers may reach energy consumption levels comparable to many industrial facilities and small-sized towns. Therefore, innovative and transparent energy policies should be applied to improve energy consumption and deliver the best performance. This paper compares, analyzes and evaluates various energy efficiency policies, which shut down underutilized machines, on an extensive set of data-center environments. Data envelopment analysis (DEA) is then conducted for the detection of the best energy efficiency policy and data-center characterization for each case. This analysis evaluates energy consumption and performance indicators for natural DEA and constant returns to scale (CRS). We identify the best energy policies and scheduling strategies for high and low data-center demands and for medium-sized and large data-centers; moreover, this work enables data-center managers to detect inefficiencies and to implement further corrective actions.

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.

Introducing Energy Efficiency Metrics for Server and Data Centers

2011 ◽  
Author(s):  
Abdlmonem H. Beitelmal ◽  
Drazen Fabris
Keyword(s):  
Energy Efficiency ◽  
Data Center ◽  
Data Centers ◽  
Cooling Efficiency ◽  
Idle Power ◽  
Relevant Variables ◽  
The Cost ◽  
Evaluation Of Data

New servers and data center metrics are introduced to facilitate proper evaluation of data centers power and cooling efficiency. These metrics will be used to help reduce the cost of operation and to provision data centers cooling resources. The most relevant variables for these metrics are identified and they are: the total facility power, the servers’ idle power, the average servers’ utilization, the cooling resources power and the total IT equipment power. These metrics can be used to characterize and classify servers and data centers performance and energy efficiency regardless of their size and location.

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 Cloud-based services for electronic civil registration and vital statistics systems

2019 ◽  
Vol 38 (S1) ◽  
Author(s):  
Brett McDowall ◽  
Samuel Mills
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Vital Statistics ◽  
Information And Communications Technology ◽  
Cloud Provider ◽  
Civil Registration ◽  
Continuous Innovation ◽  
Use Of Data ◽  
Virtual Data Center ◽  
And Storage

Abstract This paper examines the hosting options for electronic civil registration and vital statistics (CRVS) systems, particularly the use of data centers versus cloud-based solutions. A data center is a facility that houses computer systems and associated hardware and software components, such as network and storage systems, power supplies, environment controls, and security devices. An alternative to using a data center is cloud-based hosting, which is a virtual data center hosted by a public cloud provider. The cloud is used on a pay-as-you-go basis and does not require purchasing and maintaining of hardware for data centers. It also provides more flexibility for continuous innovation in line with evolving information and communications technology.

Exergy-Based Optimization Strategies for Multi-Component Data Center Thermal Management: Part I — Analysis

2005 ◽  
Author(s):  
Amip J. Shah ◽  
Van P. Carey ◽  
Cullen E. Bash ◽  
Chandrakant D. Patel
Keyword(s):  
Thermal Management ◽  
Data Center ◽  
Air Conditioning ◽  
Data Centers ◽  
Heat Dissipation ◽  
Real Data ◽  
Management Systems ◽  
Prior Work ◽  
Operating Strategy ◽  
Air Space

As heat dissipation in data centers rises by orders of magnitude, inefficiencies such as recirculation will have an increasingly significant impact on the thermal manageability and energy efficiency of the cooling infrastructure. For example, prior work has shown that for simple data centers with a single Computer Room Air-Conditioning (CRAC) unit, an operating strategy that fails to account for inefficiencies in the air space can result in suboptimal performance. To enable system-wide optimality, an exergy-based approach to CRAC control has previously been proposed. However, application of such a strategy in a real data center environment is limited by the assumptions inherent to the single-CRAC derivation. This paper addresses these assumptions by modifying the exergy-based approach to account for the additional interactions encountered in a multi-component environment. It is shown that the modified formulation provides the framework necessary to evaluate performance of multi-component data center thermal management systems under widely different operating circumstances.

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