Comparison and Evaluation of Different Monitoring Methods in a Data Center Environment

The operation of today’s data centers increasingly relies on environmental data collection and analysis to operate the cooling infrastructure as efficiently as possible and to maintain the reliability of IT equipment. This in turn emphasizes the importance of the quality of the data collected and their relevance to the overall operation of the data center. This study presents an experimentally based analysis and comparison between two different approaches for environmental data collection; one using a discrete sensor network, and another using available data from installed IT equipment through their Intelligent Platform Management Interface (IPMI). The comparison considers the quality and relevance of the data collected and investigates their effect on key performance and operational metrics. The results have shown the large variation of server inlet temperatures provided by the IPMI interface. On the other hand, the discrete sensor measurements showed much more reliable results where the server inlet temperatures had minimal variation inside the cold aisle. These results highlight the potential difficulty in using IPMI inlet temperature data to evaluate the thermal environment inside the contained cold aisle. The study also focuses on how industry common methods for cooling efficiency management and control can be affected by the data collection approach. Results have shown that using preheated IPMI inlet temperature data can lead to unnecessarily lower cooling set points, which in turn minimizes the potential cooling energy savings. It was shown in one case that using discrete sensor data for control provides 20% more energy savings than using IPMI inlet temperature data.

Download Full-text

Expanded Assessment of a Practical Thermally Aware Energy-Optimized Load Placement Strategy for Open-Aisle, Air-Cooled Data Centers

Journal of Electronic Packaging ◽

10.1115/1.4024945 ◽

2013 ◽

Vol 135 (3) ◽

Cited By ~ 2

Author(s):

Dustin W. Demetriou ◽

H. Ezzat Khalifa

Keyword(s):

Real Time ◽

Data Center ◽

Data Centers ◽

Thermal Environment ◽

Inlet Temperature ◽

Airflow Rate ◽

Time Data ◽

Improve Energy Efficiency ◽

Air Temperatures ◽

Holistic Optimization

This paper expands on the work presented by Demetriou and Khalifa (Demetriou and Khalifa, 2013, “Thermally Aware, Energy-Based Load Placement in Open-Aisle, Air-Cooled Data Centers,” ASME J. Electron. Packag., 135(3), p. 030906) that investigated practical IT load placement options in open-aisle, air-cooled data centers. The study found that a robust approach was to use real-time temperature measurements at the inlet of the racks to remove IT load from the servers with the warmest inlet temperature. By considering the holistic optimization of the data center load placement strategy and the cooling infrastructure optimization, for a range of data center IT utilization levels, this study investigated the effect of ambient temperatures on the data center operation, the consolidation of servers by completely shutting them off, a complementary strategy to those presented by Demetriou and Khalifa (Demetriou and Khalifa, 2013, “Thermally Aware, Energy-Based Load Placement in Open-Aisle, Air-Cooled Data Centers,” ASME J. Electron. Packag., 135(3), p. 030906) for increasing the IT load beginning with servers that have the coldest inlet temperature and finally the development of load placement rules via either static (i.e., during data center benchmarking) or dynamic (using real-time data from the current thermal environment) allocation. In all of these case studies, by using a holistic optimization of the data center and associated cooling infrastructure, a key finding has been that a significant amount of savings in the cooling infrastructure's power consumption is seen by reducing the CRAH's airflow rate. In many cases, these savings can be larger than providing higher temperature chilled water from the refrigeration units. Therefore, the path to realizing the industry's goal of higher IT equipment inlet temperatures to improve energy efficiency should be through both a reduction in air flow rate and increasing supply air temperatures and not necessarily through only higher CRAH supply air temperatures.

Download Full-text

Impact of Fan Airflow of IT Equipment on Thermal Environment and Energy Consumption of a Data Center

Energies ◽

10.3390/en13236166 ◽

2020 ◽

Vol 13 (23) ◽

pp. 6166

Author(s):

Naoki Futawatari ◽

Yosuke Udagawa ◽

Taro Mori ◽

Hirofumi Hayama

Keyword(s):

Energy Consumption ◽

Total Energy ◽

Data Center ◽

Air Conditioning ◽

Thermal Environment ◽

Operating Efficiency ◽

Inlet Temperature ◽

Total Energy Consumption ◽

Air Supply ◽

Machine Room

Energy-saving in regard to heating, ventilation, and air-conditioning (HVAC) in data centers is strongly required. Therefore, to improve the operating efficiency of the cooling equipment and extend the usage time of the economizer used for cooling information-technology equipment (ITE) in a data center, it is often the case that a high air-supply temperature within the range in which the ITE can be sufficiently cooled is selected. In the meantime, it is known that when the ambient temperature of the ITE rises, the speed of the built-in cooling fan increases. Acceleration of the built-in fan is thought to affect the cooling performance and energy consumption of the data center. Therefore, a method for predicting the temperature of a data center—which simply correlates supply-air temperature with ITE inlet temperature by utilizing existing indicators, such as air-segregation efficiency (ASE)—is proposed in this study. Moreover, a method for optimizing the total energy consumption of a data center is proposed. According to the prediction results obtained under the assumption of certain computer-room air-conditioning (CRAC) conditions, by lowering the ITE inlet temperature from 27 °C to 18 °C, the total energy consumption of the machine room is reduced by about 10%.

Download Full-text

Improving Prediction Accuracy Concerning the Thermal Environment of a Data Center by Using Design of Experiments

Energies ◽

10.3390/en13184595 ◽

2020 ◽

Vol 13 (18) ◽

pp. 4595

Author(s):

Naoki Futawatari ◽

Yosuke Udagawa ◽

Taro Mori ◽

Hirofumi Hayama

Keyword(s):

Design Of Experiments ◽

Data Center ◽

Prediction Accuracy ◽

Data Centers ◽

Cfd Simulation ◽

Thermal Environment ◽

Parameter Tuning ◽

Inlet Temperature ◽

Cfd Model ◽

Ventilation Efficiency

In data centers, heating, ventilation, and air-conditioning (HVAC) consumes 30–40% of total energy consumption. Of that portion, 26% is attributed to fan power, the ventilation efficiency of which should thus be improved. As an alternative method for experimentations, computational fluid dynamics (CFD) is used. In this study, “parameter tuning”—which aims to improve the prediction accuracy of CFD simulation—is implemented by using the method known as “design of experiments”. Moreover, it is attempted to improve the thermal environment by using a CFD model after parameter tuning. As a result of the parameter tuning, the difference between the result of experimental-measurement results and simulation results for average inlet temperature of information-technology equipment (ITE) installed in the ventilation room of a test data center was within 0.2 °C at maximum. After tuning, the CFD model was used to verify the effect of advanced insulation such as raised-floor fixed panels and show the possibility of reducing fan power by 26% while keeping the recirculation ratio constant. Improving heat-insulation performance is a different approach from the conventional approach (namely, segregating cold/hot airflow) to improving ventilation efficiency, and it is a possible solution to deal with excessive heat generated in data centers.

Download Full-text

A Statistical Approach to Real-Time Updating and Automatic Scheduling of Physical Models

Volume 2: Thermal Management; Data Centers and Energy Efficient Electronic Systems ◽

10.1115/ipack2013-73042 ◽

2013 ◽

Author(s):

Huijing Jiang ◽

Xinwei Deng ◽

Vanessa Lopez ◽

Hendrik Hamann

Keyword(s):

High Resolution ◽

Real Time ◽

Physical Model ◽

Energy Management ◽

Data Center ◽

Data Centers ◽

Thermal Environment ◽

Physical Models ◽

Sensor Data ◽

Automatic Scheduling

Energy consumption of data center has increased dramatically due to the massive computing demands driven from every sector of the economy. Hence, data center energy management has become very important for operating data centers within environmental standards while achieving low energy cost. In order to advance the understanding of thermal management in data centers, relevant environmental information such as temperature, humidity and air quality are gathered through a network of real-time sensors or simulated via sophisticated physical models (e.g. computational fluid dynamics models). However, sensor readings of environmental parameters are collected only at sparse locations and thus cannot provide a detailed map of temperature distribution for the entire data center. While the physics models yield high resolution temperature maps, it is often not feasible, due to computational complexity of these models, to run them in real-time, which is ideally required for optimum data center operation and management. In this work, we propose a novel statistical modeling approach to updating physical model outputs in real-time and providing automatic scheduling for re-computing physical model outputs. The proposed method dynamically corrects the discrepancy between a steady-state output of the physical model and real-time thermal sensor data. We show that the proposed method can provide valuable information for data center energy management such as real-time high-resolution thermal maps. Moreover, it can efficiently detect systematic changes in a data center thermal environment, and automatically schedule physical models to be re-executed whenever significant changes are detected.

Download Full-text

Spatially and temporally distributed data foraging decisions in disciplinary field science

Cognitive Research Principles and Implications ◽

10.1186/s41235-021-00296-z ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Cristina G. Wilson ◽

Feifei Qian ◽

Douglas J. Jerolmack ◽

Sonia Roberts ◽

Jonathan Ham ◽

...

Keyword(s):

Data Collection ◽

Simulated Data ◽

Environmental Data ◽

Distributed Data ◽

Field Science ◽

In Situ Data ◽

Disciplinary Field ◽

Variable Space ◽

Query Selection ◽

Fitting Error

AbstractHow do scientists generate and weight candidate queries for hypothesis testing, and how does learning from observations or experimental data impact query selection? Field sciences offer a compelling context to ask these questions because query selection and adaptation involves consideration of the spatiotemporal arrangement of data, and therefore closely parallels classic search and foraging behavior. Here we conduct a novel simulated data foraging study—and a complementary real-world case study—to determine how spatiotemporal data collection decisions are made in field sciences, and how search is adapted in response to in-situ data. Expert geoscientists evaluated a hypothesis by collecting environmental data using a mobile robot. At any point, participants were able to stop the robot and change their search strategy or make a conclusion about the hypothesis. We identified spatiotemporal reasoning heuristics, to which scientists strongly anchored, displaying limited adaptation to new data. We analyzed two key decision factors: variable-space coverage, and fitting error to the hypothesis. We found that, despite varied search strategies, the majority of scientists made a conclusion as the fitting error converged. Scientists who made premature conclusions, due to insufficient variable-space coverage or before the fitting error stabilized, were more prone to incorrect conclusions. We found that novice undergraduates used the same heuristics as expert geoscientists in a simplified version of the scenario. We believe the findings from this study could be used to improve field science training in data foraging, and aid in the development of technologies to support data collection decisions.

Download Full-text

Simulation of an Acid Gas Removal Unit Using a DGA and MDEA Blend Instead of a Single Amine

Chemical Product and Process Modeling ◽

10.1515/cppm-2019-0044 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Umer Zahid

Keyword(s):

Energy Savings ◽

Circulation Rate ◽

Inlet Temperature ◽

Acid Gas ◽

Gas Processing ◽

Gas Removal ◽

Processing Plants ◽

Operational Energy ◽

Commercial Process ◽

The Impact

AbstractMost of the industrial acid gas removal (AGR) units employ chemical absorption process for the removal of acid gases from the natural gas. In this study, two gas processing plants operational in Saudi Arabia have been selected where two different amines n1amely, diglycolamine (DGA) and monoethanol amine (MDEA) are used to achieve the sweet gas purity with less than 4 ppm of H2S. This study performed a feasibility simulation of AGR unit by utilizing the amine blend (DGA+MDEA) for both plants instead of a single amine. The study used a commercial process simulator to analyze the impact of process variables such as amine circulation rate, amine strength, lean amine temperature, regenerator inlet temperature, and absorber and regenerator pressure on the process performance. The results reveal that when the MDEA (0–15 wt. %) is added to DGA, marginal energy savings can be achieved. However, significant operational energy savings can be made when the DGA (0–15 wt. %) is blended with MDEA being the main amine.

Download Full-text

A COMPUTER-THERMOCOUPLE INTERFACING SYS FOR TIME-TEMPERATURE DATA COLLECTION FROM THERMAL FOOD PROCESSES

Journal of Food Process Engineering ◽

10.1111/j.1745-4530.1978.tb00214.x ◽

1978 ◽

Vol 2 (4) ◽

pp. 323-336 ◽

Cited By ~ 1

Author(s):

RICHARD A. ROOP ◽

ARTHUR F. BADENHOP

Keyword(s):

Data Collection ◽

Temperature Data ◽

Food Processes

Download Full-text

Getting Enough Sleep: On the Importance of Collecting Longitudinal Data from Wearables to Assess Sleep Quality and Seasonal Effects on Variability in Daily Activity (Preprint)

10.2196/preprints.31807 ◽

2021 ◽

Author(s):

María Óskarsdóttir ◽

Anna Sigridur Islind ◽

Elias August ◽

Erna Sif Arnardóttir ◽

Francois Patou ◽

...

Keyword(s):

Physical Activity ◽

Heart Rate ◽

Data Collection ◽

Sleep Quality ◽

Daily Activity ◽

Hospital Environment ◽

Sensor Data ◽

Seasonal Patterns ◽

Sleep Patterns ◽

Sleep Habits

BACKGROUND The method considered the gold standard for recording sleep is a polysomnography, where the measurement is performed in a hospital environment for 1-3 nights. This requires subjects to sleep with a device and several sensors attached to their face, scalp, and body, which is both cumbersome and expensive. For longer studies with actigraphy, 3-14 days of data collection is typically used for both clinical and research studies. OBJECTIVE The primary goal of this paper is to investigate if the aforementioned timespan is sufficient for data collection, when performing sleep measurements at home using wearable and non-wearable sensors. Specifically, whether 3-14 days of data collection sufficient to capture an individual’s sleep habits and fluctuations in sleep patterns in a reliable way for research purposes. Our secondary goals are to investigate whether there is a relationship between sleep quality, physical activity, and heart rate, and whether individuals who exhibit similar activity and sleep patterns in general and in relation to seasonality can be clustered together. METHODS Data on sleep, physical activity, and heart rate was collected over a period of 6 months from 54 individuals in Denmark aged 52-86 years. The Withings Aura sleep tracker (non-wearable) and Withings Steel HR smartwatch (wearable) were used. At the individual level, we investigated the consistency of various physical activities and sleep metrics over different time spans to illustrate how sensor data from self-trackers can be used to illuminate trends. RESULTS Significant variability in standard metrics of sleep quality was found between different periods throughout the study. We show specifically that in order to get more robust individual assessment of sleep and physical activity patterns through wearable and non-wearable devices, a longer evaluation period than 3-14 days is necessary. Additionally, we found seasonal patterns in sleep data related to changing of the clock for Daylight Saving Time (DST). CONCLUSIONS We demonstrate that over two months worth of self-tracking data is needed to provide a representative summary of daily activity and sleep patterns. By doing so, we challenge the current standard of 3-14 days for sleep quality assessment and call for rethinking standards when collecting data for research purposes. Seasonal patterns and DST clock change are also important aspects that need to be taken into consideration, and designed for, when choosing a period for collecting data. Furthermore, we suggest using consumer-grade self-trackers (wearable and non-wearable ones) to support longer term evaluations of sleep and physical activity for research purposes and, possibly, clinical ones in the future.

Download Full-text