HTA corrosion resistant technology for free cooling

The advent of the big data era, the rapid development of mobile internet, and the rising demand of cloud computing services require increasingly more compute capability from their data center. This compute increase will most likely come from higher rack and room power densities or even construction of new Internet data centers. But an increase in a data center’s business-critical IT equipment (servers, hubs, routers, wiring patch panels, and other network appliances), not to mention the infrastructure needed to keep these devices alive and protected, encroaches on another IT goal: to reduce long-term energy usage. Large Internet Data Centers are looking at every possible way to reduce the cooling cost and improve efficiency. One of the emerging trends in the industry is to move to higher ambient data center operation and use air side economizers. However, these two trends can have significant implications for corrosion risk in data centers. The prevailing practice surrounding the data centers has often been “The colder, the better.” However, some leading server manufacturers and data center efficiency experts share the opinion that data centers can run far hotter than they do today without sacrificing uptime, and with a huge savings in both cooling related costs and CO2 emissions. Why do we need to increase the temperatures? To cool data center requires huge refrigeration system which is energy hog and also cost of cooling infrastructure, maintenance cost and operation cost are heavy cost burden. Ahuja et al [1] studied cooling path management in data center at typical operating temperature as well as higher ambient operating temperatures. High Temperatures and Corrosion Resistance technology will reduce the refrigeration output and how this innovation will open up new direction in data centers. Note that, HTA is not to say that the higher the better. Before embracing HTA two keys points need to be addressed and understood. Firstly, server stability along with optimal temperature from data center perspective. Secondly, corrosion resistant technology. With Fresh air cooling the server has to bear with the seasons and diurnal variation of temperatures and these can be over 35 degree C, therefore to some extent, we have to say, HTA design is the premise of corrosion resistant design. In this paper, we present methods to realize precise HTA operation along with corrosive resistant technology. This is achieved through an orchestrated collaboration between the IT and cooling infrastructures.

Deployment of air-side economizers in data centers is rapidly gaining acceptance to reduce the cost of energy by reducing the hours of operation of CRAC units. Use of air-side economizers has the associated risk of introducing gaseous and particulate contamination into data centers, thus, degrading the reliability of Information Technology (IT) equipment. Sulfur-bearing gaseous contamination is of concern because it attacks the copper and silver metallization of the electronic components causing electrical opens and/or shorts. Particulate contamination with low deliquescence relative humidity is of concern because it becomes wet and therefore electrically conductive under normal data center relative humidity conditions. IT equipment manufacturers guarantee the reliability of their equipment operating in environment within ISA 71.04-2013 severity level G1 and within the ASHRAE recommended temperature-relative humidity envelope. The challenge is to determine the reliability degrading effect of contamination severity levels higher than G1 and the temperature and humidity allowable ranges A1–A3 well outside the recommended range. This paper is a first attempt at addressing this challenge by studying the cumulative corrosion damage to IT equipment operated in an experimental data center located in Dallas, known to have contaminated air with ISA 71.04-2013 severity level G2. The data center is cooled using an air-side economizer. This study serves several purposes including: the correlation of equipment reliability to levels of airborne corrosive contaminants and the study of the degree of reliability degradation when the equipment is operated, outside the recommended envelope, in the allowable temperature-relative humidity range in geographies with high levels of gaseous and particulate contamination. The operating and external conditions of a modular data center, located in a Dallas industrial area, using air-side economizer is described. The reliability degradation of servers exposed to outside air via an airside economizer was determined qualitatively examining the corrosion of components in the servers and comparing the results to the corrosion of components in a non-operating server stored in a protective environment. The corrosion-related reliability of the servers over almost the life of the product was related to continuous temperature and relative humidity for the duration of the experiment. This work provides guidance for data center administration for similar environment. From an industry perspective, it should be noted that in the four years of operation in the hot and humid Dallas climate using only evaporative cooling or fresh air cooling, we have not seen a single server failure in our research pod. That performance should highlight an opportunity for significant energy savings for data center operators in a much broader geographic area than currently envisioned with evaporative cooling.

Internet Data Centers have increasingly gained importance in recent days. organizations require their data centers to run with minimum expenditure to improve profits, and the service requests need to be fulfilled with the minimum service delay possible. Increasing profits and minimizing service delay are challenges faced by data centers on a day-to-day basis. The demand response in a data center is a crucial challenge to be optimized because of its direct correlation with profits and delay in an internet data center. In this paper, meta-heuristic swarm algorithms are analyzed to find the best possible profits and minimum possible delays that can arise in a data center for a particular number of active servers and the amount of workload processed by the data center. The methods are verified with real-world data for pricing schemes of electricity, power consumption, request loads and data center specifications.

Due to the advancement of the telecommunication and information technology (IT) industry, internet data centers (IDCs) have become widespread in the public and private sectors. As such, energy demand in the center has also become increasingly prominent. Several technologies on energy management have been studied to determine the options available to minimize the energy required to operate the data center as well as reduce greenhouse gas emissions. The cooling system is required to remove the high heat dissipated by the IT electronic components especially the servers in order to ensure safe and reliable working condition. However, it utilizes more than one-third of the total energy consumption in the data center. In this study, the energy efficiency technologies that are usually applied to cooling systems in data centers were reviewed. The aim is to find out the strategies that will reduce the energy consumption of the cooling system since the cooling demand in data center is all year round. Prior to that, the performance metric tool that is mostly used in analyzing data center efficiency was discussed. The conventional cooling system technologies that are utilized in data centers were also provided. Lastly, innovative cooling technologies for future solutions in data centers were discussed.

Relevance. Data centers are a specialized facility that is an interconnected system of IT infrastructure, engineering infra-structure, equipment (server and network) and parts of which are located in a building or room connected to external networks, both engineering and telecommunications. Due to the significant heat generation of these systems, there is a need to increase the efficiency of cooling systems through efficient heat recovery.The purpose. Calculation of the energy balance of a typical data center designed for operation in central Russia.Methods. The developed energy model of a data center is a mathematical description of all energy processes in a building, including heat transfer processes, the effects of ventilation and air conditioning, the operation of lighting, electrical appliances and other equipment, and the operation of engineering systems.Results. According to the simulation results, the specific energy consumption was 1,353 kWh/m2, the specific annual cost of energy resources was 8,572.90 rubles/m2. The following key results were obtained: energy consumed by data center modules – 38,399.53 MWh/year; energy consumed by IT equipment – 31,685.20 MWh/year; PUE (it represents the ratio of the total energy consumed by the data center to the energy directly used by the IT equipment) – 1.21.Conclusion. Data on annual energy consumption, the cost of consumed energy resources and the amount of CO2 released into the atmosphere during the production of energy resources were obtained. A monthly energy consumption chart has been developed by category (heating, cooling, fans, heat recovery, pumps, interior lighting, outlet equipment, servers). In the course of further research, it is planned to consider priority schemes for the utilization of thermal energy of the data center.

Internet data centers, typically distributed across the world in order to provide timely and reliable Internet service, have been increasingly pressurized to reduce their carbon footprint and electricity cost. Particularly, data centers will soon be required to abide by carbon capping polices which impose carbon footprint limits to encourage brown energy conservation. We propose an online algorithm, called OnlineCC, for minimizing the operational cost while satisfying the carbon footprint reduction target of a set of geo-distributed data centers. OnlineCC makes use of Lyapunov optimization technique while operating without long-term future information, making it attractive in the presence of uncertainties associated with data center information e.g., input workload. We prove that OnlineCC achieves a near optimal operational cost (electricity cost) compared to the optimal algorithm with future information, while bounding the potential violation of carbon footprint target, depending on the Lyapunov control parameter, namely V. We also give a heuristic for finding V which significantly shortens the search space to adjust its value. Finally, we perform a trace-based simulation study and a small scale experiment to complement the analysis. The results show that OnlineCC reduces cost by more than 18% compared to a prediction-based online solution while resulting in equal or smaller carbon footprint.

Data centers are energy-intensive facilities, with over 95% of their total cooling load attributed to the heat generated by information technology equipment (ITE). Various energy-saving techniques have been employed to enhance data center efficiency and to reduce power usage effectiveness (PUE). Among these, economizers using outdoor air for cooling are the most effective for addressing year-round cooling demands. Despite the simplicity of the load composition, analyzing data center cooling systems involves dynamic considerations, such as weather conditions, system conditions, and economizer control. A PUE interpretation tool was specifically developed for use in data centers, aimed at addressing the simplicity of data center loads and the complexity of system analysis. The tool was verified through a comparison with results from DesignBuilder implementing the EnergyPlus algorithm. Using the developed tool, a comparative analysis of economizer strategies based on the PUE distribution was conducted, with the aim of reducing the PUE of data centers across various climatic zones. The inclusion of evaporative cooling (EC) further improved cooling efficiency, leading to reductions in PUE by approximately 0.02 to 0.05 in dry zones. Additionally, wet zones exhibited PUE reductions, ranging from approximately 0.03 to 0.07, with the implementation of indirect air-side economizer (IASE). Sensitivity and uncertainty analysis were further conducted. The computer room air handler (CRAH) supply temperature and CRAH temperature difference were the most influential factors affecting the annual PUE. For the direct air-side economizer (DASE) and DASE + EC systems, higher PUE uncertainty was observed in zones 1B, 3B, 4B, and 5B, showing ranges of 1.17–1.39 and 1.15–1.17, respectively. In the case of the IASE and IASE + EC systems, higher PUE uncertainty was noted in zones 0A, 0B, 1A, 1B, and 2A, with ranges of 1.22–1.43 and 1.17–1.43, respectively. The distinctive innovation of the tool developed in this study is characterized by its integration of specific features unique to data centers. It streamlines the computation of cooling loads, thus minimizing the burden of input, and delivers energy consumption data for data center cooling systems with a level of precision comparable to that of commercial dynamic energy analysis tools. It provides data center engineers with a valuable resource to identify optimal alternatives and system design conditions for data centers. This empowers them to make informed decisions based on energy efficiency enhancements, thereby strengthening their ability to improve energy efficiency.

As the number of internet data centers (IDC) increases, the energy consumption of modern data centers is growing rapidly, which takes an increasing part of global electricity consumption. The data center load, as a flexible electricity load which can be scheduled spatially and temporally is increasingly drawing attentions in the research of demand response and distributed network planning. However, there are hardly software which is dedicated to simulating the electrical characteristics of data centers, and most of the existing data center simulation software ignores the characteristics of data center power supply devices, which is not fit for verifying the demand response method proposed by the researches in electrical field. In view of this, we modified the most popular data center simulation software CloudSim by adding modules which can model the data center power supply system. A simulation of a typical day is implemented on the modified platform, which has verify the effectiveness of the modified software.

The 2007 US EPA report to Congress (US EPA, 2007) on the state of energy consumption in data centers brought to light the true energy inefficiencies built into today’s data centers. Marquez et al. (2008) conducted an initial analysis on the productivity of a Pacific Northwest National Lab computer using The Green Grid’s Data Center Energy Productivity metric (The Green Grid, 2008). Their study highlights how the Top500 ranking of computers disguises the serious energy inefficiency of today’s High Performance Computing data centers. In the rapidly expanding Cloud Computing space, the race will be won by the providers that deliver the lowest cost of computing — such cost is heavily influenced by the operational costs incurred by data centers. As a means to address the urgent need to lower the cost of computing, solution providers have been intensely focusing on real-time monitoring, visualization, and control/management of data centers. The monitoring aspect involves the widespread use of networks of sensors that are used to monitor key data center environmental variables such as temperature, relative humidity, air flow rate, pressure, and energy consumption. Such data is then used to visualize and analyze data center problem areas (e.g., hotspots), which is then followed by control/management actions designed to alleviate such problem areas. The authors have been researching the operational benefits of a network of sensors tied in to a software package that uses the data to visualize, analyze, and control/manage the data center cooling system and IT Equipment for maximum operational efficiency. The research is being conducted in a corporate production data center that is networked in to the authors’ company’s global network of data centers. Results will be presented that highlight the operational benefits that are realizable through real-time monitoring and visualization.

A scalable framework that uses a cost-adaptive load sharing among Internet data centers (IDC) deployed over a large scale geographic area is introduced in this study. The key idea is to efficiently allocate load to the IDCs, in order to reduce the cost of cloud computing. Data centers are designed and built based on their expected peak load. Thus, reducing the expected peak load can significantly reduce their capital expenses (e.g.; their server acquisition cost). Previous studies addressing the issue of reducing the cost of cloud computing focused on reducing its electricity cost, while ignoring the capital cost, which is significantly higher [14]. Thus, these methods are not scalable, and their potential contribution and possible implementation are not clear. Simply minimizing the IDC electricity cost without conducting load balancing among the IDCs may eventually increase the total cost associated with global cloud computing, due to the potential increment in the IDC peak load. This study shows that balancing load, electricity cost, and networking cost, subject to service delay constraints, the entire cost of cloud computing (CapEx plus OpEx) can be further reduced, in comparison with previous studies.

Data center facilities, which house thousands of servers, storage devices and computing hardware, arranged in 2 meter high racks are providing many thermal challenges. Each rack can dissipate 10–15 kW, and with facilities as large as tens of thousands of square feet, the net power dissipated is typically on the order of several MW. The cost to power these facilities alone can be millions of dollars a year, with the cost to provide adequate cooling not far behind. Significant savings can be realized for the end user by improved design methodology of these high power density data centers. The fundamental need for improved characterization is motivated by inadequacies of simple energy balances to identify local ‘hot spots’ and ultimately provide a reliable modeling framework by which the data centers of the future can be designed. Recent attempts in computational fluid dynamics (CFD) modeling of data centers have been based around a simple rack model, either as a uniform heat generator or specified temperature rise across the rack. This desensitizes the solution to variations of heat load and corresponding flow rate needed to cool the servers throughout the rack. Heat generated at the smaller scales (the chip level) produces changes in the larger length scales of the data center. Accurate simulations of these facilities should attempt to resolve the range of length scales present. In this paper, a multi-scale model where each rack is subdivided into a series of sub-models to better mimic the behavior of individual servers inside the data center is proposed. A Reynolds-averaged Navier-Stokes CFD model of a 110 m2 (1,200 ft2) representative data center with the raised floor cooling scheme was constructed around this multi-scale rack model. Each of the 28 racks dissipated 4.23 kW, giving the data center a power density of 1076 W/m2 (100 W/ft2) based on total floor space. Parametric studies of varying heat loads within the rack and throughout the data center were performed to better characterize the interactions of the sub-rack scale heat generation and the data center. Major results include 1) the presence of a nonlinear thermal response in the upper portion of each rack due to recirculation effects and 2) significant changes in the surrounding racks (up to 10% increase in maximum temperature) observed in response to changes in rack flow rate (50% decrease).

With the evolution of the communication though network and the storage technology, the construction of internet data center (IDC) is also accelerating its development. Based on the analysis of the current status of IDC construction, the article also studies the construction of its network, sever, memory system, application system, self-service system, and computer room to provide macroscopically with the reference to setting up IDC in the colleges.

Data centres are large energy consumers, which have become more energy efficient in recent years due to increased awareness of opportunities, increasing energy costs and corporate social responsibility pressures. A lifecycle assessment approach identifies two other significant areas of impact: the embodied impact of IT equipment and mechanical and electrical plant, and the electricity source used in operational and embodied processes. Many data centre operators publicise their energy efficiency, some also report on their carbon footprint. However, there is a need for simple tools in order to help operators better understand and quantify the embodied impact and inform green procurement. Focussing purely on energy efficiency may cause a burden shift, e.g. by replacing equipment with more efficient equipment but increasing the embodied impact. The total environmental impact could remain the same/increase, but with the perception of a ‘greener’ data centre. Although currently there is limited data available for data centre life cycle assessments and the process is resource intensive, research has identified which factors significantly impact a facility’s environmental impact. This knowledge should be used in the design process and throughout the data centre lifecycle to minimise data centre environmental impact. Practical application: As the data centre industry continues to grow and its sustainability receives closer scrutiny, it is important to increase awareness of where its highest environment impacts lie and analyse the factors which influence this. This can help inform policy and decision-making to support the design and operation of data centres which are truly more sustainable. It is important that this research is not just theoretical but can translate into practical actions which can be implemented in a cost-effective manner for the benefit of everyone. The current focus in industry has been energy consumption and energy efficiency; many practices which were considered innovative a few years ago have now become standard best practice. It is time to examine which other areas should be prioritised for improvement.

More powerful and dense data centers require more reliable cooling infrastructures to support them. An increase in energy demand reflects on energy cost, increased greenhouse gases emission and overall cost for upcoming data centers. Therefore, conservation and management of data center energy usage is of great importance. In a typical data center, almost all the electrical power supplied to the IT equipment is converted into waste heat, hence powerful cooling systems are required in order to keep the components below the maximum allowable temperature. For this reason, aside from the IT equipment itself, the highest energy consumer in a data center is the cooling system (more than 30% of the total consumption). Hence, it is important to investigate possible solutions to optimize the thermal management, both by reducing the energy consumption of the cooling systems, and by optimizing the waste heat recovery potential. On this basis, the present paper reports an optimization study carried out in order to achieve energy savings and improved heat recovery potential for a hybrid air-liquid cooled server. In particular, this study investigates the effect of two different baffle designs on the air-cooling efficiency in the server, and on the potential of waste heat recovery at the server outlet. A 2D CFD model is employed to perform a numerical convective heat transfer analysis of the server. Entropy generation minimization (EGM)in conjunction with a multi-objective genetic algorithm (MOGA)are employed to evaluate the cooling system ability to minimize the pump power and unify the air outlet heat stream while keeping the server component temperatures below prescribed constraints.

Modern-day data center administrators are finding it increasingly difficult to lower the costs incurred in mechanical cooling of their IT equipment. This is especially true for high-performance computing facilities like Artificial Intelligence, Bitcoin Mining, and Deep Learning, etc. Airside Economization or free air cooling has been out there as a technology for a long time now to reduce the mechanical cooling costs. In free air cooling, under favorable ambient conditions of temperature and humidity, outside air can be used for cooling the IT equipment. In doing so, the IT equipment is exposed to sub-micron particulate/gaseous contaminants that might enter the data center facility with the cooling airflow. The present investigation uses a computational approach to model the airflow paths of particulate contaminants entering inside the IT equipment using a commercially available CFD code. A Discrete Phase Particle modeling approach is chosen to calculate trajectories of the dispersed contaminants. Standard RANS approach is used to model the airflow in the airflow and the particles are superimposed on the flow field by the CFD solver using Lagrangian particle tracking. The server geometry was modeled in 2-D with a combination of rectangular and cylindrical obstructions. This was done to comprehend the effect of change in the obstruction type and aspect ratio on particle distribution. Identifying such discrete areas of contaminant proliferation based on concentration fields due to changing geometries will help with the mitigation of particulate contamination related failures in data centers.