Data Center Workload Placement for Energy Efficiency

2007 ◽  
Author(s):  
Cullen Bash ◽  
George Forman
Keyword(s):  
Energy Efficiency ◽  
Heat Production ◽  
Data Center ◽  
Data Centers ◽  
Real Data ◽  
Cooling Efficiency ◽  
The Past ◽  
Computer Power

Data center costs for computer power and cooling have been steadily increasing over the past decade. Much work has been done in recent years on understanding how to improve the delivery of cooling resources to IT equipment in data centers, but little attention has been paid to the optimization of heat production by considering the placement of application workload. Because certain physical locations inside the data center are more efficient to cool than others, this suggests that allocating heavy computational workloads onto those servers that are in more efficient places might bring substantial savings. This paper explores this issue by introducing a workload placement metric that considers the cooling efficiency of the environment. Additionally, results from a set of experiments that utilize this metric in a thermally isolated portion of a real data center are described. The results show that the potential savings is substantial and that further work in this area is needed to exploit the savings opportunity.

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.

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.

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.

Research on Intelligent Air Conditioning System of Data Center

2014 ◽  
Vol 602-605 ◽  
pp. 928-932
Author(s):  
Min Li ◽  
Yun Wang ◽  
Zheng Qian Feng ◽  
Wang Li
Keyword(s):  
Heat Exchange ◽  
Energy Saving ◽  
Data Center ◽  
Air Conditioning ◽  
Data Centers ◽  
Cooling Efficiency ◽  
Exchange System ◽  
Air Conditioning System ◽  
Running Time ◽  
Heat Exchange System

By studying the energy-saving technologies of air-conditioning system in data centers, we designed a intelligent air conditioning system, improved the cooling efficiency of air conditioning system through a reasonable set of hot and cold aisles, reduced the running time of HVAC by using the intelligent heat exchange system, an provided a reference for energy saving research of air conditioning system of data centers.

Optimization of Data Center Cooling Efficiency Using Reduced Order Flow Modeling Within a Flow Network Modeling Approach

2014 ◽  
Author(s):  
K. Fouladi ◽  
A. P. Wemhoff ◽  
L. Silva-Llanca ◽  
A. Ortega
Keyword(s):  
Thermal Efficiency ◽  
Data Center ◽  
Data Centers ◽  
Network Models ◽  
Cooling Efficiency ◽  
Exergy Destruction ◽  
Cfd Simulations ◽  
Flow Network ◽  
Reduced Order ◽  
Efficiency Optimization

Much of the energy use by data centers is attributed to the energy needed to cool the data centers. Thus, improving the cooling efficiency and thermal management of data centers can translate to direct and significant economic benefits. However, data centers are complex systems containing a significant number of components or sub-systems (e.g., servers, fans, pumps, and heat exchangers) that must be considered in any synergistic data center thermal efficiency optimization effort. The Villanova Thermodynamic Analysis of Systems (VTAS) is a flow network tool for performance prediction and design optimization of data centers. VTAS models the thermodynamics, fluid mechanics, and heat transfer inherent to an entire data center system, including contributions by individual servers, the data center airspace, and the HVAC components. VTAS can be employed to identify the optimal cooling strategy among various alternatives by computing the exergy destruction of the overall data center system and the various components in the system for each alternative. Exergy or “available energy” has been used to identify components and wasteful practices that contribute significantly in cooling inefficiency of data centers including room air recirculation — premature mixing of hot and cold air streams in a data center. Flow network models are inadequate in accurately predicting the magnitude of airflow exergy destruction due to simplifying assumptions and the three-dimensional nature of the flow pattern in the room. On the other hand, CFD simulations are time consuming, making them impractical for iterative-based design optimization approaches. In this paper we demonstrate a hybrid strategy, in which a proper orthogonal decomposition (POD) based airflow modeling approach developed from CFD simulation data is implemented in VTAS for predicting the room airflow exergy destruction. The reduced order POD tool in VTAS provides higher accuracy than 1-D flow network models and is computationally more efficient than 3-D CFD simulations. The present VTAS – POD tool has been applied to a data center cell to illustrate the use of exergy destruction minimization as an objective function for data center thermal efficiency optimization.

Energy-Efficiency Through the Integration of Information and Communications Technology Management and Facilities Controls

2009 ◽  
Author(s):  
Michael K. Patterson ◽  
Michael Meakins ◽  
Dennis Nasont ◽  
Prasad Pusuluri ◽  
William Tschudi ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Air Temperature ◽  
Data Center ◽  
Temperature Sensor ◽  
Data Centers ◽  
Cooling System ◽  
Information And Communications Technology ◽  
Demonstration Project ◽  
Communications Technology ◽  
Control Loop

Increasing energy-efficient performance built into today’s servers has created significant opportunities for expanded Information and Communications Technology (ICT) capabilities. Unfortunately the power densities of these systems now challenge the data center cooling systems and have outpaced the ability of many data centers to support them. One of the persistent problems yet to be overcome in the data center space has been the separate worlds of the ICT and Facilities design and operations. This paper covers the implementation of a demonstration project where the integration of these two management systems can be used to gain significant energy savings while improving the operations staff’s visibility to the full data center; both ICT and facilities. The majority of servers have a host of platform information available to the ICT management network. This demonstration project takes the front panel temperature sensor data from the servers and provides that information over to the facilities management system to control the cooling system in the data center. The majority of data centers still use the cooling system return air temperature as the primary control variable to adjust supply air temperature, significantly limiting energy efficiency. Current best practices use a cold aisle temperature sensor to drive the cooling system. But even in this case the sensor is still only a proxy for what really matters; the inlet temperature to the servers. The paper presents a novel control scheme in which the control of the cooling system is split into two control loops to maximize efficiency. The first control loop is the cooling fluid which is driven by the temperature from the physically lower server to ensure the correct supply air temperature. The second control loop is the airflow in the cooling system. A variable speed drive is controlled by a differential temperature from the lower server to the server at the top of the rack. Controlling to this differential temperature will minimize the amount of air moved (and energy to do so) while ensuring no recirculation from the hot aisle. Controlling both of these facilities parameters by the server’s data will allow optimization of the energy used in the cooling system. Challenges with the integration of the ICT management data with the facilities control system are discussed. It is expected that this will be the most fruitful area in improving data center efficiency over the next several years.

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.

Autonomous Detection of Thermal Anomalies in Data Centers

2009 ◽  
Author(s):  
Manish Marwah ◽  
Ratnesh K. Sharma ◽  
Wilfredo Lugo
Keyword(s):  
Anomaly Detection ◽  
Data Center ◽  
Data Centers ◽  
Principal Component ◽  
Thermal Anomaly ◽  
Real Data ◽  
Detection Methods ◽  
Large Temperature ◽  
Efficient Operation ◽  
Thermal Anomalies

In recent years, there has been a significant growth in number, size and power densities of data centers. A significant part of data center power consumption is attributed to the cooling infrastructure, consisting of computer air conditioning units (CRACs), chillers and cooling towers. For energy efficient operation and management of the cooling resources, data centers are beginning to be extensively instrumented with temperature sensors. While this allows cooling actuators, such as CRAC set point temperature, to be dynamically controlled and data centers operated at higher temperatures to save energy, it also increases chances of thermal anomalies. Furthermore, considering that large data centers can contain thousands to tens of thousands of such sensors, it is virtually impossible to manually inspect and analyze the large volumes of dynamic data generated by these sensors, thus necessitating autonomous mechanisms for thermal anomaly detection. Also, in addition to threshold-based detection methods, other mechanisms of anomaly detection are also necessary. In this paper, we describe the commonly occurring thermal anomalies in a data center. Furthermore, we describe — with examples from a production data center — techniques to autonomously detect these anomalies. In particular, we show the usefulness of a principal component analysis (PCA) based methodology to a large temperature sensor network. Specifically, we examine thermal anomalies such as those related to misconfiguration of equipment, blocked vent tiles, faulty sensor and CRAC related anomalies. Furthermore, several of these anomalies normally go undetected since no temperature thresholds are violated. We present examples of the thermal anomalies and their detection from a real data center.

Improving Cooling Efficiency by Using Mixed Tiles to Control Airflow Uniformity of Perforated Tiles in a Data Center Model

2017 ◽  
Author(s):  
Long Phan ◽  
Sadhana Bhusal ◽  
Cheng-Xian Lin
Keyword(s):  
Power Density ◽  
Simulation Study ◽  
Data Center ◽  
Data Centers ◽  
Industrial Sector ◽  
Cooling Efficiency ◽  
Direct Impact ◽  
Flow Through ◽  
Energy Consumptions ◽  
Heat Loads

Data centers in recent years have grown so fast so that their energy consumptions become a big issue in the industrial sector. One of the strategies to make better use of energy in data centers is to improve the efficiency in cooling. As the load density in data centers increases dramatically over the years, the number of computer room air handlers (CRAHs) are also increased to accommodate the high cooling demands. However, the number of CRAH units and their layouts really affect the air flow through the perforated tiles. Non-uniform airflow distributions in the perorated tiles in the cold aisles cause inefficient cooling of all the servers mounted in racks in data centers. Application of necessary strategies to minimize airflow non-uniformity is therefore very important because of its direct impact on the power density capacity. In this paper, a simulation study to examine how computer room air handler (CRAH) positions, the number of operating units, and tile types affect the airflow uniformity in selected data center models. Also, the placement of mixed tiles in cold aisles to regulate the airflow through the perforated tiles to accommodate greater heat loads from server racks is evaluated.

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