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Geographically distributed cloud data centers (DCs) consume enormous amounts of energy to meet the ever-increasing processing and storage demands of users. The brown energy generated using fossil fuels is expensive and significantly contributes to global warming. Considering the environmental impact caused by the high carbon emissions and relatively high energy cost of brown energy, we propose the integration of renewable energy sources (RES), especially solar and wind energy, with brown energy to power cloud data centers. In our earlier study, we addressed the intermittency of renewable energy sources, where we replaced the random initialization of artificial neural network (ANN) edge weights with the harmony search algorithm (HSA)-optimized assignment of weights. This study incorporated reliably forecast solar and wind energy into the input parameters of our proposed green energy manager (GEM), for cost minimization, carbon emission minimization, and better energy management of cloud DCs, to make our current study more reliable and trustworthy. Four power sources, on-site solar energy and wind energy, off-site solar energy and wind energy, energy stored in energy storage devices, and brown energy, were considered in this study and simulations were carried out for three different cases. The simulation results showed that case 1 (all brown) was 58% more expensive and caused 71% higher carbon emissions than case 2.1 (cost minimization). Case 1 (all brown) was 39% more expensive and had 80% higher carbon emissions than case 2.2 (carbon emission minimization). The simulation results justify the necessity and importance of the GEM, and finally the results proved that our proposed GEM is less expensive and more environmentally friendly. [ABSTRACT FROM AUTHOR]

The rapid growth in computing and data transmission has significant energy and environmental implications. While there is considerable interest in waste heat emission and reuse in commercial data centers, opportunities in academic data centers remain largely unexplored. In this study, real-time onsite waste heat data were collected from a typical academic data center and an analysis framework was developed to determine the quality and quantity of waste heat that can be contained for reuse. In the absence of a comprehensive computer room monitoring system, real-time thermal data were collected from the data center using two arrays of thermometers and thermo-anemometers in the server room. Additionally, a computational fluid dynamics model was used to simulate temperature distribution and identify "hot spots" in the server room. By simulating modification of the server room with a hot air containment system, the return air temperature increased from 23 to 46 °C and the annual waste heat energy increased from 377 to 2004 MWh. Our study emphasizes the importance of containing waste heat so that it can be available for reuse, and also, that reusing the waste heat has value in not releasing it to the environment. [ABSTRACT FROM AUTHOR]

As demand for data centers (DC) has increased rapidly, so has their electricity consumption. Cooling DCs is essential to maintain optimal temperatures for the operation of servers and equipment. The consequence of the cooling process is that most of the electricity consumed in DCs, including cooling, is eventually dissipated as heat that is released into the atmosphere without any useful application. Recovering and reusing waste heat offers a sustainable solution to reduce primary energy consumption and minimize the environmental impact. Using waste heat from DCs to heat buildings can significantly improve the energy efficiency and environmental sustainability of DCs. Therefore, this research analyzes the existing potential of waste heat recovery from data centers in Latvia and proposes a system dynamic modelling approach for evaluation of the future impact of waste heat on the national heat supply. The overall waste heat generated by DCs in 2022 was 51.37 GWh at a temperature of 65 °C. By 2050, the total heat energy production potential from DCs will increase to 257 GWh, with 201 GWh being utilized. [ABSTRACT FROM AUTHOR]

The highlighted energy consumption of Internet data center (IDC) in China has become a pressing issue with the implementation of the Chinese dual carbon strategic goal. This paper provides a comprehensive review of cooling technologies for IDC, including air cooling, free cooling, liquid cooling, thermal energy storage cooling and building envelope. Firstly, the environmental requirements for the computer room and the main energy consumption items for IDC are analyzed. The evaluation indicators and government policies for promoting green IDC are also summarized. Next, the traditional cooling technology is compared to four new cooling technologies to find effective methods to maximize energy efficiency in IDC. The results show that traditional cooling consumes a significant amount of energy and has low energy efficiency. The application of free cooling can greatly improve the energy efficiency of IDC, but its actual implementation is highly dependent on geographical and climatic conditions. Liquid cooling, on the other hand, has higher energy efficiency and lower PUE compared to other cooling technologies, especially for high heat density servers. However, it is not yet mature and its engineering application is not widespread. In addition, thermal energy storage (TES) based cooling offers higher energy efficiency but must be coupled with other cooling technologies. Energy savings can also be achieved through building envelope improvements. Considering the investment and recovery period for IDC, it is essential to seek efficient cooling solutions that are suitable for IDC and take into account factors such as IDC scale, climate conditions, maintenance requirements, etc. This paper serves as a reference for the construction and development of green IDC in China. [ABSTRACT FROM AUTHOR]

Data centres, though a necessary part of modern society, are being stigmatised for consuming vast amounts of electricity for their operational and cooling needs. Due to Ireland's reliance on fossil fuels to meet the increased energy demand of data centres, the data centres are contributing significantly to Ireland's total carbon emissions. As much of this energy is expelled from data centres as waste heat energy, the potential for recycling some of this wasted heat energy was explored using environmentally friendly systems from recent publications. The recovered waste heat energy was applied in a vertical farming system, and the benefits of this waste heat to the vertical farm were analysed and quantified in two scenarios. Using conservative estimates, it was predicted that each vertical farm could be between 5–23% the size of the data centre and produce enough food to feed between 14–61 adults their daily calorie needs, and between 13–58 people their daily fresh produce requirements, depending on the scenario applied. For a more accurate prediction, each vertical farm would have to be assessed on a case-by-case basis, as there is no current research in this area. However, there was not enough data available on Irish data centres to perform these calculations. [ABSTRACT FROM AUTHOR]

Green and low-carbon has become the main theme of global energy development. Data centers are the core of the digital age, carrying huge arithmetic demand. Data centers must implement green low-carbon energy efficiency management to improve energy efficiency, reduce energy waste and carbon emissions, and achieve sustainable development. As a result, an intelligent management strategy for dynamic energy efficiency of data center networks with Artificial Intelligence (AI) fitting control is proposed. Firstly, a Long Short-Term Memory (LSTM) network is used for long sequence trend prediction to predict the temperature of the data center in the next sequence using the temperature of the past 15 sequences and the power consumption of the equipment as parameters. Then, based on the prediction results, the intelligent air conditioning controller based on Deep Q-Network (DQN) is designed to update the parameters by using the gradient of double-Q network and error backpropagation, and the optimal control action is selected by using the ε-greedy strategy to ensure that the prediction of the hotspot does not occur. Experiments show that the average absolute errors of temperature prediction for supply air, return air, cold aisle as well as hot aisle are 0.32 °C, 0.21 °C, 0.36 °C and 0.19 °C, respectively. The Power Usage Effectiveness (PUE) and Water Usage Effectiveness (WUE) decreased by an average of 2.6% and 2.5%, respectively. The method achieves the purpose of predicting future temperatures and intelligently controlling the output so that the data center can satisfy the premise of normal operation and thus achieve more efficient energy use. [ABSTRACT FROM AUTHOR]

Data Centres serve as the foundation for digital technologies in the energy sector, enabling advanced analytics, optimization, and automation. However, their rapid growth can exert a substantial influence on the environment due to their energy consumption, water utilization, and production of electronic waste. This research begins with an energy overview of the setup and operations of data centres, highlighting their key components and infrastructure, and emphasizing their crucial role in managing energy resources and driving the energy sector's digital technologies. Building upon this understanding, a holistic framework is proposed to tackle energy sustainability concerns in data centres, with a focus on energy-related aspects. The framework places emphasis on three primary sustainability metrics, namely energy efficiency, water consumption, and waste management. It underscores the significance of green building design principles and energy-efficient equipment as crucial constituents of sustainable data centre infrastructure. The framework delineates optimal energy operational best practices encompassing virtualization and consolidation, effective cooling tactics, and energy management and monitoring, all aimed at reducing energy consumption and enhancing energy performance. Furthermore, the framework emphasizes the significance of incorporating energy-related sustainability metrics into decision-making procedures and adhering to regulatory standards for energy efficiency. Through adherence to this framework, data centres' environmental impact can be mitigated and a positive contribution towards a sustainable future can be made, particularly in the realm of energy conservation and optimization. [ABSTRACT FROM AUTHOR]

Modern data center power distribution networks place greater demands on the stability and reliability of power supply. Growing network computing demands and complex network environments can cause network congestion, which in turn leads to network traffic overload and power supply equipment overload. Therefore, network congestion is one of the most important problems faced by data center power distribution networks. In this paper, we propose an approach called ACC-RL based on reinforcement learning (RL), which can effectively avoid network congestion and improve energy performance. ACC-RL models the congestion control task as a Partially Observable Markov Decision Process (POMDP). It is independent of the estimated value function and supports deterministic policies. It also sets the reward value function using real-time network information such as the transmission rate, RTT, and switch queue length, with the target transmission rate as the target equilibrium point. ACC-RL is highly general, can be trained on datasets running in different network environments, and generates a robust congestion control policy. The experimental results show that ACC-RL can solve the congestion problem without any predefined scenarios in different network environments. It can control the network traffic well, thus ensuring the stability and reliability of the power supply in the distribution network. We conduct network simulation experiments through NS-3. We set up different scenarios for experiments and data analysis in many-to-one, all-to-all, and long–short network environments. Compared with the popular rule-based congestion control algorithms such as TIMELY, DCQCN, and HPCC, ACC-RL shows different degrees of energy performance advantages in network metrics such as fairness, link utilization, and throughput. [ABSTRACT FROM AUTHOR]

The cooling system is the auxiliary equipment that consumes the most energy in a data center, accounting for about 30 to 50% of the total energy consumption. In order to effectively reduce the energy consumption of a data center, it is very important to improve the heat exchange efficiency at the chip level. Compared with air cooling, single-phase cold plate liquid cooling, and immersion liquid cooling, the flat loop heat pipe (FLHP) is considered to be a better chip-level cooling solution for data centers. It has extremely high heat transfer efficiency and heat flux variability, and it can avoid the operation risk caused by liquid entering the server. In this paper, a FLHP with an evaporator designed with a "Tesla valve" flow channel configuration is developed. Experiments on the FLHP are carried out, focusing on the installation angles and cooling condition factors. The results show that an inclination angle of 20° is the critical point of the influence of gravity on the performance of the FLHP; to ensure good operation of the FLHP, the installation angle should be greater than 20°. The equivalent heat transfer coefficients of the FLHP condenser under different cooling conditions are calculated. It is found that water cooling can provide higher cooling heat transfer coefficients with lower energy consumption and operating noise. Additionally, the heat transfer limit, operating temperature uniformity, and start-up stability of the FLHP are significantly improved under water cooling conditions. The maximum heat load of the FLHP is up to 230 W, and the temperature difference of the evaporator surface can be controlled within 0.5 °C, under 20 °C water cooling. Finally, using the FLHP for thermal management of the chip, its heat transfer efficiency is 166 and 41% higher than that of air cooling and water cooling, respectively. [ABSTRACT FROM AUTHOR]

The immersion phase-change cooling technology utilizes the latent heat of the cooling liquid to dissipate heat by directly contacting the cooling liquid with the heat-generating electronic chip, which can meet the cooling requirements of current high heat flux density data centers. In this paper, the effect of different factors on the heat dissipation performance of immersion phase-change cooling technology was explored through numerical simulation. The results show that, under certain power conditions, the inlet temperature and flow rate of the cooling water in the condensation module, as well as the different arrangement of servers, have a significant impact on the heat dissipation performance of the entire system. The inlet water temperature mainly affects the chip temperature after stabilization. With the decrease in the inlet temperature, the chip surface temperature decreases significantly. The inlet water flow rate mainly affects the time required for the heat exchange to reach the desired temperature. With the increase in the inlet flow rate, the required cooling time is shortened. As the spacing between servers increases, the thermal safety and stability of the entire system increase. When the spacing between servers increases from 5 mm to 15 mm, the highest temperature and the temperature uniformity coefficient between the systems decrease significantly. When the spacing increases from 15 mm to 25 mm, the highest temperature and the temperature uniformity coefficient decrease slightly. These results can provide useful information for the designers of immersion phase-change cooling systems to improve the cooling efficiency of data centers, save energy, and ensure the safe operation of related computers, servers, and communication systems. [ABSTRACT FROM AUTHOR]

This paper addresses the energy conservation problem in computing systems. The focus is on energy-efficient routing protocols. We formulated and solved a network-wide optimization problem for calculating energy-aware routing for the recommended network configuration. Considering the complexity of the mathematical models of data center networks and the limitations of calculating routing by solving large-scale optimization problems, and methods described in the literature, we propose an alternative solution. We designed and developed several efficient heuristics for equal-cost multipath (ECMP) and Valiant routing that reduce the energy consumption in the computer network interconnecting computing servers. Implementing these heuristics enables the selection of routing paths and relay nodes based on current and predicted internal network load. The utility and efficiency of our methods were verified by simulation. The test cases were carried out on several synthetic network topologies, giving encouraging results. Similar results of using our efficient heuristic algorithm and solving the optimization task confirmed the usability and effectiveness of our solution. Thus, we produced well-justified recommendations for energy-aware computing system design to conclude the paper. [ABSTRACT FROM AUTHOR]

Contemporary megawatt-scale data centers have emerged to meet the increasing demand for online cloud services and big data analytics. However, in such large-scale data centers, servers of different generations are installed gradually year by year, making the data center heterogeneous in computing capability and energy efficiency. Furthermore, due to different processor architectures, complex and diverse load dynamic changing, business coupling, and other reasons, operators pay great attention to processor hardware power consumption and server aggregation energy efficiency. Therefore, the simulation and analysis of the energy efficiency characteristics of data center servers under different processor architectures can help operators understand the energy efficiency characteristics of data centers and make the optimal task scheduling strategy. This is very beneficial for improving the energy efficiency of the production system and the entire data center. The Escope simulator designed in this study can simulate the online quantity (placement strategy) of different types of servers in the data center and the optimal operating range of the servers. The purpose of this is to analyze the energy efficiency characteristics of all servers in the data center and provide data center operators with the energy efficiency and energy proportionality characteristics of different servers, improve server utilization, and perform reasonable scheduling. Through the simulation experiment of Escope, it can be proved that running the server at the highest energy efficiency point or running the server under full load cannot improve the energy efficiency of the entire data center. The simulation algorithm provided by Escope can select the optimal set of servers and their corresponding utilization. Escope can set up a variety of simulation strategies, and data center operators can simulate data center energy efficiency according to their own needs. Escope can also calculate the power cost savings of introducing new servers in the data center, which provides an essential reference for operators to purchase servers and design data centers. [ABSTRACT FROM AUTHOR]

Liquid-cooled servers can be deployed to reduce the energy consumption and environmental footprint of hybrid-cooled data centers. A computational fluid dynamics (CFD) model can bring extremely useful insights and results for thermal simulations of air- and liquid-cooled servers in a single environment. In this study, a conjugate heat transfer (CHT) numerical model is developed and validated with experimental data to simulate heat transfer from the CPU to the air and cold plate considering the effect of thermal paste. The cooling performance of an in-house developed cold plate design is thoroughly investigated via the validated CHT model. A dataset containing one hundred samples of various flow, thermal and workload conditions was generated using the Latin hypercube sampling (LHS) method, which was further utilized in the series of CHT simulations. Finally, a novel empirical equation is proposed for the prediction of heat transfer from the CPU to the air. The accuracy of the proposed equation is confirmed by comparing estimated and simulated results for a test dataset. A thermal analysis of a rack containing air and liquid-cooled servers is performed using the presented approach. The simulation results reveal that the proposed compact model can be used reliably for the thermal simulation of a hybrid-cooled data center. [ABSTRACT FROM AUTHOR]

Workload prediction is essential in cloud data centers (CDCs) for establishing scalability and resource elasticity. However, the workload prediction accuracy in the cloud data center could be better due to noise, redundancy, and low performance for workload prediction. This paper designs a hierarchical tree-based deep convolutional neural network (T-CNN) model with sheep flock optimization (SFO) to enhance CDCs' power efficiency and workload prediction. The kernel method is used to preprocess historical information from the CDCs. Additionally, T-CNN model weight parameters are optimized using SFO. The suggested TCNN-SFO technology has successfully reduced excessive power consumption while correctly forecasting the incoming demand. Further, the proposed model is assessed using two benchmark datasets: Saskatchewan HTTP traces and NASA. The developed model is executed in a Java tool. Therefore, associated with existing methods, the developed technique has achieved higher accuracy of 20.75%, 19.06%, 29.09%, 23.8%, and 20.5%, as well as lower energy consumption of 20.84%, 18.03%, 28.64%, 30.72%, and 33.74% when validating the Saskatchewan HTTP traces dataset. It has also achieved higher accuracy of 32.95%, 12.05%, 32.65%, and 26.54%. [ABSTRACT FROM AUTHOR]

This study investigates the use of transfer learning and modular design for adapting a pretrained model to optimize energy efficiency and heat reuse in edge data centers while meeting local conditions, such as alternative heat management and hardware configurations. A Physics-Informed Data-Driven Recurrent Neural Network (PIDD RNN) is trained on a small scale-model experiment of a six-server data center to control cooling fans and maintain the exhaust chamber temperature within safe limits. The model features a hierarchical regularizing structure that reduces the degrees of freedom by connecting parameters for related modules in the system. With a RMSE value of 1.69, the PIDD RNN outperforms both a conventional RNN (RMSE: 3.18), and a State Space Model (RMSE: 2.66). We investigate how this design facilitates transfer learning when the model is fine-tuned over a few epochs to small dataset from a second set-up with a server located in a wind tunnel. The transferred model outperforms a model trained from scratch over hundreds of epochs. [ABSTRACT FROM AUTHOR]

The number of data centers worldwide is increasing year by year, mostly because of the development of cloud services and applications. In the near future, the rate of construction of data centers will grow, with a corresponding increase in their electrical energy consumption. The requirements of the reliability of the electrical power supply of data centers are one of the highest among industrial power consumers, since uninterrupted power supply is critically important for the continuous functioning of server hardware. The assessment of electrical power supply reliability is one of the most important parts of the design process of data centers. However, the speed of the development of new power equipment does not always make it possible to use classical probabilistic and statistical methods for reliability assessment. Therefore, the development of new methods for reliability assessment based on alternative approaches, which can eliminate the disadvantages of probabilistic and statistical methods, are of great interest. This paper discusses the alternative method for analyzing the reliability of electrical power supply for the case of data centers. The method defines the reliability through the internal information of the system that characterizes the system's topology, flows of information, energy, and matter in the system. [ABSTRACT FROM AUTHOR]

The thermal management and reduction of energy consumption in cooling systems have become major trends with the continued growth of high heat dissipation data centers and the challenging energy situation. However, the existing studies have been limited to studying the influences of individual factors on energy saving and thermal management and have not been systematically summarized. Thus, this paper reviews the key factors in achieving thermal management and reducing energy consumption in each cooling system, the corresponding research, and optimization methods. To achieve these goals, in this paper, literature surveys on data center cooling systems are investigated. For data center air cooling, thermal management is mainly related to the uniform distribution of hot and cold air. Adjusting the porosity of perforated tiles can reduce energy consumption. For liquid cooling and free cooling systems, climate conditions, cooling system structural design, coolant type, and flow rate are key factors in achieving thermal management and reducing energy consumption. This paper provides the power usage effectiveness (PUE) values of the cooling systems in some cases. A summary of the key factors can provide directions for research on thermal management and energy reduction, and a summary of previous research can provide a basis for future optimization. [ABSTRACT FROM AUTHOR]

Urban heat sources from urban infrastructure and buildings could meet ~10% of the European building heating demand. There is, however, limited information on how to use them. The EU project ReUseHeat has generated much of the existing knowledge on urban waste heat recovery implementation. Heat recovery from a data center, hospital and from water were demonstrated. Additionally, the project generated knowledge of stakeholders, risk profile, bankability and business models. The recovery of urban waste heat is characterized by high potential, high competitiveness compared to other heating alternatives, high avoidance of GHG emissions, payback within three years and low utilization. These characteristics reveal that barriers for increased utilization exist. The barriers are not technical. Instead, the absence of a waste heat EU level policy adds risk. Other showstoppers are low knowledge on the urban waste heat opportunity and new stakeholder relationships being needed for successful recovery. By combining key results and lessons learned from the project this article outlines the frontier of urban waste heat recovery research and practice in 2022. [ABSTRACT FROM AUTHOR]

A novel energy-saving hybrid cooling system that combines a forced-cooling cycle and a free-cooling cycle was developed to increase the energy efficiency of cooling systems in year-round operation of an internet data center with high heat loads. This system effectively utilizes a dam deep water source to reduce energy consumption in internet data centers. The hybrid cooling system operates in forced-cooling mode when the entering water temperature exceeds the mode change temperature of 9 °C, but switches to free-cooling mode when the ambient temperature falls below the mode change temperature. In this paper, the cooling performance of the hybrid system was assessed under various operating conditions based on entering water temperature fluctuation. Because the cooling effectiveness of this type of system is highly dependent on the outside climate, its usefulness and suitability for different periods and zones must be investigated. The annual energy saving performance of the new system was estimated and compared to a conventional cooling system in terms of the integrated coefficient of performance, based on the hourly weather air temperature and water temperature bin data collected from 16 cities of different climate zones in South Korea. The experimental findings revealed that the novel hybrid internet data center cooling system showed a 67% annual operating performance over a conventional air source internet data center cooling system due to the adoption of a dam deep water source. [ABSTRACT FROM AUTHOR]

In the age of the digital economy, the data center is the most crucial piece of infrastructure. The issue of the excessive power consumption of a data center's cooling system needs to be addressed as the national objective of "peak carbon and carbon neutrality" is increasingly promoted. In this study, a microchannel-separated heat pipe-cooling system with evaporative condensation is introduced. The system may switch between three modes of operation in response to changes in outdoor air quality parameters, thereby maximizing the utilization of natural cooling sources while lowering data centers' cooling costs. The purpose of this paper is to analyze the energy-saving potential of the hybrid system through experimental tests. The results show that 114.4% is the ideal liquid-loading rate for the heat pipe system. Under working conditions in Xi'an, the annual operating hours of the three modes accounted for 47.2%, 6.1%, and 46.7%. The hybrid cooling system may save 62.04% of the energy used annually compared to the standard cooling system and the cooling system in the server room thanks to its yearly average COP of 9.43. [ABSTRACT FROM AUTHOR]

Owing to the high energy demand of buildings, which accounted for 36% of the global share in 2020, they are one of the core targets for energy-efficiency research and regulations. Hence, coupled with the increasing complexity of decentralized power grids and high renewable energy penetration, the inception of smart buildings is becoming increasingly urgent. Data-driven building energy management systems (BEMS) based on deep reinforcement learning (DRL) have attracted significant research interest, particularly in recent years, primarily owing to their ability to overcome many of the challenges faced by conventional control methods related to real-time building modelling, multi-objective optimization, and the generalization of BEMS for efficient wide deployment. A PRISMA-based systematic assessment of a large database of 470 papers was conducted to review recent advancements in DRL-based BEMS for different building types, their research directions, and knowledge gaps. Five building types were identified: residential, offices, educational, data centres, and other commercial buildings. Their comparative analysis was conducted based on the types of appliances and systems controlled by the BEMS, renewable energy integration, DR, and unique system objectives other than energy, such as cost, and comfort. Moreover, it is worth considering that only approximately 11% of the recent research considers real system implementations. [ABSTRACT FROM AUTHOR]

As one of the most promising thermal management solutions, spray cooling has the advantages of high heat-transfer coefficient and maintaining a low temperature of the cooling surface. By summarizing the influential factors and practical applications of spray cooling, the current challenges and bottlenecks were indicated so as to prompt its potential applications in the future. Firstly, this paper reviewed the heat-transfer mechanism of spray cooling and found that spray cooling is more advantageous for heat dissipation in high-power electronic devices by comparing it with other cooling techniques. Secondly, the latest experimental studies on spray cooling were reviewed in detail, especially the effects of spray parameters, types of working fluid, surface modification, and environmental parameters on the performance of cooling system. Afterwards, the configuration and design of the spray cooling system, as well as its applications in the actual industry (data centers, hybrid electric vehicles, and so on) were enumerated and summarized. Finally, the scientific challenges and technical bottlenecks encountered in the theoretical research and industrial application of spray cooling technology were discussed, and the direction of future efforts were reasonably speculated. [ABSTRACT FROM AUTHOR]

Modeling IT equipment is of critical importance for the simulations of flow and thermal structures in air cooled data centers. Turbulent flow undergoes a significant pressure drop through the server due to the energy losses originating from the internal components. Therefore, there is an urgent need to develop a fast and an accurate method for the calculation of pressure losses inside server components for data center applications. In this study, high resolution numerical simulations were performed on an OCP (Open Compute Project) server under various inlet flow rates for inactive and active conditions. Meanwhile, one key challenge of modeling complete geometry of the server results from using an intense mesh even for a single server. To address this challenge, the server was modeled as a porous zone to mimic inertia and viscous resistance in a realistic way. Comparison of the results of porous and complete models showed that the proposed model could calculate pressure drop accurately even when the number of cells in the server was reduced to 0.3% of the complete model. Porosity coefficients were determined from the numerical simulations conducted in a broad range of air discharge for both active and inactive conditions. Errors in the calculation of pressure drop may result in a significant deviation in the prediction of the temperature rise over the server. Thus, the present model can effectively be used for the fast and accurate prediction of pressure drop inside a server component rather than solving internal flow on an intense mesh, while simulating airflow inside an air-cooled data center, which is crucial for the design safety of data centers. Finally, calculated porosity coefficients can be used for the prediction of the pressure drop in a server, while designing data centers based on numerical simulations. [ABSTRACT FROM AUTHOR]

The growing number of data centers consumes a vast amount of energy for processing. There is a desire to reduce the environmental footprint of the IT industry, and one way to achieve this is to use renewable energy sources. A challenge with using renewable resources is that the energy output is irregular as a consequence of the intermittent nature of this form of energy. In this paper, we propose a simple and yet efficient latency-aware workload scheduler that creates an energy-agile workload, by deferring tasks with low latency sensitivity to periods with excess renewable energy. The scheduler also increases the overall efficiency of the data center, by packing the workload into as few servers as possible, using neural-network-based predictions of resource usage on an individual task basis to avoid unnecessarily provisioning an excess number of servers. The scheduler was tested on a subset of real-world workload traces, and real-world wind-power generation data, simulating a small-scale data center co-located with a wind turbine. Extensive experimental results show that the devised scheduler reduced the number of servers doing work in periods of low wind-power production up to 93% of the time, by postponing tasks with a low latency sensitivity to a later interval. [ABSTRACT FROM AUTHOR]

Thermodynamic, exergetic and thermoeconomic analyses were performed on two types of double-effect LiBr–water absorption refrigeration systems (ARS) for use with a 5-kW high-temperature proton exchange membrane fuel cell (HT-PEMFC) as a heat source. Proper temperatures of the high-pressure generator, combined generator and condenser, condenser, absorber and evaporator were determined to meet the requirements of constant cooling demands for data center operations. The heat balance of the combined unit of generator and condenser in the industrial double-effect LiBr-water ARS is important for determining the flow rate of the primary vapor refrigerant from the high-pressure generator. The industrial double-effect ARS system, whose analysis has not been studied analytically, outperformed the series double-effect system and provided 6.5 kW of cooling capacity with a coefficient of performance of 0.99. The unit cost of chilled water estimated by the modified productive structure analysis (MOPSA) method is approximately 7.18 USD/GJ (=0.026 US$/kWh). Effective exergetic efficiency of HT-PEMFC with the industrial ARS increases to 57.6% from 47.0%. [ABSTRACT FROM AUTHOR]

Data center servers located in thermal hotspot regions receive inlet air at a higher than the set temperature and thus generate comparatively high outlet temperature. Consequently, there is a rise in energy that is consumed to cool down the servers that otherwise would undergo reliability hazards. The workload deployment across the servers should be resilient to thermal hotspots to ensure smooth performance. In a heterogeneous data center environment, an equally important fact is the placement of the servers in a thermal hotspot-aware manner to lower the peak outlet temperatures. These approaches can be applied proactively with the help of outlet temperature prediction. This paper presents the hotspot adaptive workload deployment algorithm (HAWDA) and hotspot aware server relocation algorithm (HASRA) based on thermal profiling regarding outlet temperature prediction. HAWDA deploys workload on servers in a thermal-efficient manner and HASRA optimizes the server location in thermal hotspot regions to lower the peak outlet temperatures. Performance comparison is carried out to analyze the efficacy of HAWDA against the TASA and GRANITE algorithms. Results suggest that HAWDA provides average peak utilization of the servers similar to GRANITE and TASA without additional burden on the cooling mechanism, with and without server relocation, as HAWDA minimizes the peak outlet temperature. [ABSTRACT FROM AUTHOR]

Commercial solutions in the area of data center cooling available on the market are universal solutions that use dedicated control methods and are to able function properly in a wide range of working conditions. The functional limitations resulting in their application were our motivation for developing our own system architecture and a dedicated control algorithm. Historical data from an operating cooling system were analyzed. On that basis, a rule-based controller was developed, the purpose of which is to correctly switch between operating modes. The time constants of the automation actuators and the dynamics of the changes that occur in the cooling process were identified. The control strategy was experimentally validated throughout 2017. The efficacy of the second solution and the effectiveness of the control algorithm throughout the calendar year were demonstrated. Our solution allows individual control of each element of the cooling system. This enabled the extension of the operation time in the free-cooling mode by 2064 h throughout the year, a 69% increase in the COP coefficient of the cooling system and a reduction in electricity consumption by 206.9 MWh due to longer operation in the freecooling mode. [ABSTRACT FROM AUTHOR]

Improving the energy efficiency of data center has become a research focus in recent years. Previous works commonly adopted the inlet temperature constraint to optimize the thermal environment in the data center. However, the inlet temperature-aware method cannot prevent the servers from over-cooling. To cope with this issue, we propose a thermal-aware workload allocation strategy with respect to the chip temperature constraint. In this paper, we conducted a comparative evaluation of the performance between the chip and inlet temperature-aware workload allocation strategies. The workload allocation strategies adopt a POD-based heat recirculation model to characterize the thermal environment in data center. The contribution of the temperature-dependent leakage power to server power consumption is also considered. We adopted a sample data center under constant-flow and variable-flow cooling air supply to evaluate the performance of these two different workload allocation strategies. The comparison results show that the chip temperature-aware workload allocation strategy prevents the servers from over-cooling and significantly improves the energy efficiency of data center, especially for the case of variable-flow cooling air supply. [ABSTRACT FROM AUTHOR]

The worldwide demand on data storage continues to increase and both the number and the size of data centers are expanding rapidly. Energy efficiency is an important factor to consider in data centers since the total energy consumption is huge. The servers must be cooled and the performance of the cooling system depends on the flow field of the air. Computational Fluid Dynamics (CFD) can provide detailed information about the airflow in both existing data centers and proposed data center configurations before they are built. However, the simulations must be carried out with quality and trust. The k--ε model is the most common choice to model the turbulent airflow in data centers. The aim of this study is to examine the performance of more advanced turbulence models, not previously investigated for CFD modeling of data centers. The considered turbulence models are the k--ε model, the Reynolds Stress Model (RSM) and Detached Eddy Simulations (DES). The commercial code ANSYS CFX 16.0 is used to perform the simulations and experimental values are used for validation. It is clarified that the flow field for the different turbulence models deviate at locations that are not in the close proximity of the main components in the data center. The k--ε model fails to predict low velocity regions. RSM and DES produce very similar results and, based on the solution times, it is recommended to use RSM to model the turbulent airflow data centers. [ABSTRACT FROM AUTHOR]

Demand is soaring for data centers with advanced data-processing capabilities. In data centers with high-temperature information technology (IT) equipment, enormous cooling systems are operated year-round. To date, studies have aimed to improve the cooling efficiency of server-room units, but cooling-energy-consumption analysis considering the recirculation of exhaust air (EA) has not been researched to a sufficient degree. This study analyzed the cooling-energy saving effects considering the EA-recirculation and supply-air (SA)-temperature conditions when direct and indirect air-side economizers were applied to a data center in Korea. Thirteen case studies were conducted. The results showed that when the EA-recirculation ratio in the direct air-side economizer was 15%, its annual cooling-energy consumption increased by approximately 6.1% compared to the case with no recirculation. The indirect air-side economizer also exhibited an approximately 9% increase in cooling-energy consumption. On the other hand, when the SA temperature changed to 22 °C, the annual cooling-energy consumption of the direct and indirect air-side economizers decreased by approximately 67% and 55%, respectively, compared to a central chilled-water system. This indicates the importance of developing measures to prevent EA recirculation and of securing a wind path for the improvement of air-conditioning efficiency in data centers at the design stage. [ABSTRACT FROM AUTHOR]

Information technology (IT) has brought significant changes in people's lives. As an important part of the IT industry, data centres (DCs) have been rapidly growing in both the number and size over the past 40 years. Around 30% to 40% of electricity consumption in DCs is used for space cooling, thus leading to very inefficient DC operation. To identify ways to reduce the energy consumption for space cooling and increase the energy efficiency of DCs' operation, a dedicated investigation into the energy usage in DCs has been undertaken and a novel high performance dew point cooling system was introduced into a DC operational scheme. Based on the cooling load in DCs, a case study was carried out to evaluate the energy consumptions and energy usage effectiveness when using the novel dew point cooling system in different scales of DCs in various climates. It was found that by using the novel dew point cooling system, for 10 typical climates a DC can have a much lower power usage effectiveness (PUE) of 1.10 to 1.22 compared to that of 1.7 to 3.7 by using existing traditional cooling systems, leading to significantly increased energy efficiency of the DC operation. In addition, the energy performance by managing the cooling air supply at the different levels in DCs, i.e., room, row and rack level, was simulated by using a dynamic computer model. It was found that cooling air supply at rack level can provide a higher energy efficiency in DCs. Based on the above work, the energy saving potential in DCs was conducted by comparing DCs using an the novel dew point cooling system and the optimum management scheme for the cooling air supply to that using traditional air cooling systems and the same supply air management. Annual electricity consumptions for the two cases were given. It was found that by using the novel dew point cooling system and optimum management system for the cooling air supply, an 87.7~91.6% electricity consumption saving for space cooling in DCs could be achieved in 10 typical cities at 10 selected climatic conditions. [ABSTRACT FROM AUTHOR]

Climate change is recognised as one of the key challenges humankind is facing. The Information and Communication Technology (ICT) sector including data centres generates up to 2% of the global CO2 emissions, a number on par to the aviation sector contribution, and data centres are estimated to have the fastest growing carbon footprint from across the whole ICT sector, mainly due to technological advances such as the cloud computing and the rapid growth of the use of Internet services. There are no recent estimations of the total energy consumption of the European data centre and of their energy efficiency. The aim of this paper is to evaluate, analyse and present the current trends in energy consumption and efficiency in data centres in the European Union using the data submitted by companies participating in the European Code of Conduct for Data Centre Energy Efficiency programme, a voluntary initiative created in 2008 in response to the increasing energy consumption in data centres and the need to reduce the related environmental, economic and energy supply security impacts. The analysis shows that the average Power Usage Effectiveness (PUE) of the facilities participating in the programme is declining year after year. This confirms that voluntary approaches could be effective in addressing climate and energy issue. [ABSTRACT FROM AUTHOR]

A data center is a facility with a group of networked servers used by an organization for storage, management and dissemination of its data. The increase in data center energy consumption over the past several years is staggering, therefore efforts are being initiated to achieve energy efficiency of various components of data centers. One of the main reasons data centers have high energy inefficiency is largely due to the fact that most organizations run their data centers at full capacity 24/7. This results into a number of servers and switches being underutilized or even unutilized, yet working and consuming electricity around the clock. In this paper, we present Adaptive TrimTree; a mechanism that employs a combination of resource consolidation, selective connectedness and energy proportional computing for optimizing energy consumption in a Data Center Network (DCN). Adaptive TrimTree adopts a simple traffic-and-topology-based heuristic to find a minimum power network subset called 'active network subset' that satisfies the existing network traffic conditions while switching off the residual unused network components. A 'passive network subset' is also identified for redundancy which consists of links and switches that can be required in future and this subset is toggled to sleep state. An energy proportional computing technique is applied to the active network subset for adapting link data rates to workload thus maximizing energy optimization. We have compared our proposed mechanism with fat-tree topology and ElasticTree; a scheme based on resource consolidation. Our simulation results show that our mechanism saves 50%-70% more energy as compared to fat-tree and 19.6% as compared to ElasticTree, with minimal impact on packet loss percentage and delay. Additionally, our mechanism copes better with traffic anomalies and surges due to passive network provision. [ABSTRACT FROM AUTHOR]

Information technologies (IT) currently represent 2% of CO2 emissions. In recent years, a wide variety of IT solutions have been proposed, focused on increasing the energy efficiency of network data centers. Monitoring is one of the fundamental pillars of these systems, providing the information necessary for adequate decision making. However, today's monitoring systems (MSs) are partial, specific and highly coupled solutions. This study proposes a model for monitoring data centers that serves as a basis for energy saving systems, offered as a value-added service embedded in a device with low cost and power consumption. The proposal is general in nature, comprehensive, scalable and focused on heterogeneous environments, and it allows quick adaptation to the needs of changing and dynamic environments. Further, a prototype of the system has been implemented in several devices, which has allowed validation of the proposal in addition to identification of the minimum hardware profile required to support the model. [ABSTRACT FROM AUTHOR]

A review of flywheel energy storage technology was made, with a special focus on the progress in automotive applications. We found that there are at least 26 university research groups and 27 companies contributing toflywheel technology development. Flywheels are seen to excel in high-power applications, placing them closer in functionality to supercapacitors than to batteries. Examples of flywheels optimized for vehicular applications were found with a specific power of 5.5 kW/kg and a specific energy of 3.5 Wh/kg. Another flywheel system had 3.15 kW/kg and 6.4 Wh/kg, which can be compared to a state-of-the-art supercapacitor vehicular system with 1.7 kW/kg and 2.3 Wh/kg, respectively. Flywheel energy storage is reaching maturity, with 500 flywheel power buffer systems being deployed for London buses (resulting in fuel savings of over 20%), 400 flywheels in operation for grid frequency regulation and many hundreds more installed for uninterruptible power supply (UPS) applications. The industry estimates the mass-production cost of a specific consumer-car flywheel system to be 2000 USD. For regular cars, this system has been shown to save 35% fuel in the U.S. Federal Test Procedure (FTP) drive cycle. [ABSTRACT FROM AUTHOR]

This paper reviews the major researchers of liquid, immersion, and two-phase cooling. Currently, liquids are used instead of air to cool the growing data centers. Immersion cooling shows a higher heat transfer coefficient than conventional cooling (<37 W/cm2). Because the use of liquids with high global warming potentials is prohibited, the number of liquids that can be used is limited. This paper discusses the existing, relevant literature from researchers who have studied the issue at least thrice. The authors were divided into those who focused on the surface and those who formed a structure on the surface. In summary, the authors suggested the following research directions: The experimental conditions of porous foam are not diverse, and there is a concern about the separation of foam and coating into the tub. The experimental conditions of the immersion tub should also be varied according to the heat and pressure over time. Structure-level research shows higher performance than surface-level research, but an economic feasibility study is required. [ABSTRACT FROM AUTHOR]

The expansion of Internet of Things (IoT) services and the huge amount of data generated by different sensors signify the importance of cloud computing services such as Storage as a Service more than ever. IoT traffic imposes such extra constraints on the cloud storage service as sensor data preprocessing capability and load-balancing between data centers and servers in each data center. Furthermore, service allocation should be allegiant to the quality of service (QoS). In the current work, an algorithm is proposed that addresses the QoS in storage service allocation. The proposed hybrid multi-objective water cycle and grey wolf optimizer (MWG) considers different QoS objectives (e.g., energy, processing time, transmission time, and load balancing) in both the fog and cloud Layers, which were not addressed altogether. The MATLAB script is used to simulate and implement our algorithms, and services of different servers, e.g., Amazon, Dropbox, Google Drive, etc., are considered. The MWG has 7%, 13%, and 25% improvement, respectively, in comparison with multi-objective water cycle algorithm (MOWCA), k-means based GA (KGA), and non-dominated sorting genetic algorithm (NSGAII) in metric of spacing. Moreover, the MWG has 4%, 4.7%, and 7.3% optimization in metric of quality in comparison to MOWCA, KGA, and NSGAII, respectively. The new hybrid algorithm, MWG, not only yielded to the consideration of three objectives in service selection but also improved the performance compared to the works that considered one or two objective(s). The overall optimization shows that the MWG algorithm has 7.8%, 17%, and 21.6% better performance than MOWCA, KGA, and NSGAII in the obtained best result by considering different objectives, respectively. [ABSTRACT FROM AUTHOR]

While extensive research has gone into demand response techniques in data centers, the energy consumed in edge computing systems and in network data transmission remains a significant part of the computing industry's carbon footprint. The industry also has not fully leveraged the parallel trend of decentralized renewable energy generation, which creates new areas of opportunity for innovation in combined energy and computing systems. Through an interdisciplinary sociotechnical discussion of current energy, computer science and social studies of science and technology (STS) literature, we argue that a more comprehensive set of carbon response techniques needs to be developed that span the continuum of data centers, from the back-end cloud to the network edge. Such techniques need to address the combined needs of decentralized energy and computing systems, alongside the social power dynamics those combinations entail. We call this more comprehensive range "carbon-responsive computing," and underscore that this continuum constitutes the beginnings of an interconnected infrastructure, elements of which are data-intensive and require the integration of social science disciplines to adequately address problems of inequality, governance, transparency, and definitions of "necessary" tasks in a climate crisis. [ABSTRACT FROM AUTHOR]

This work addresses the problem of power quality (PQ) metrics (or indexes) suitable for DC grids, encompassing low and medium voltage applications, including electric transports, all-electric ships and aircrafts, electric vehicles, distributed generation and microgrids, modern data centers, etc. The two main pillars on which such PQ indexes are discussed and built are: (i) the physical justification, so the electric phenomena affecting DC grids and components (PV panels, fuel cells, capacitors, batteries, etc.), causing, e.g., stress of materials, aging, distortion, grid instability; and (ii) the existing standardization framework, pointing out desirable coverage and extension, similarity with AC grids standards, but also inconsistencies. For the first point, each phenomenon is discussed with quantitative conclusions on relevant thresholds: in many cases some percentage of distortion (ripple) is acceptable (stress on capacitors and storage, impact on fuel cells, and PV panels), whereas in other cases, much higher levels may be tolerated (interference to protection and monitoring devices). Standards are reviewed for indications not only of low-order harmonics and voltage fluctuations typical of old DC grid schemes, but also for high-frequency noise, including thus supraharmonics and common-mode disturbance, and filling the gap with the electromagnetic compatibility domain. However, phenomena typical of EMC and electrical safety (such as various types of overvoltages and fast transients) are excluded. Suitable PQ indexes are then reviewed, suggesting integrations and modifications, to cover the relevant phenomena and technological progress, and to better follow the normative exigencies: ripple is considered in time and frequency domain, in particular with a band limited implementation; for transients and pulsed loads, more traditional indexes based on area, energy, and half duration are confronted with indexes evaluating the power trajectory and its derivative. [ABSTRACT FROM AUTHOR]

The power system is changing towards a decarbonized one. The Kyoto protocol and the Paris climate agreement have prompted many nations to approve energy policies based on volatile renewable energy sources (RESs). However, the integration into the grid of the power generated by RESs as well as the electrification of the heating, gas and transportation sectors is becoming a huge challenge. Planning industrial and tertiary sites as net-zero energy systems (NZESs) might contribute to advance the solutions of fully integrating volatile RESs into the power system. This study aims to point out the importance of planning large energy consumer sites such as NZESs, and to depict a holistic modeling approach for this. The methodology is based on a multi-layer approach, which focuses on on-site power generation by RESs, on the improvement of energy efficiency, and on the increase of system flexibility. A qualitative case study has been conducted. It considers the planning of a Net-Zero Energy Data Center located in Germany. Results point out that new interdisciplinary and in particular social analysis methods are necessary. They might be used for accelerating the decision making process during the planning of RES-based on-site power generation systems. Besides, for computation and cooling systems, new technologies that are continuously emerging in the market should be taken into account. If well designed, they contribute to significantly decrease the whole energy demand of data center. Finally, optimal sizing of energy storage systems (electric and thermal) as well as an expedient choice of performance indicators to evaluate technology options are identified as the key factor for decreasing the external energy demand of tertiary sites, such as data center. [ABSTRACT FROM AUTHOR]

In terms of power and energy consumption, DRAMs play a key role in a modern server system as well as processors. Although power-aware scheduling is based on the proportion of energy between DRAM and other components, when running memory-intensive applications, the energy consumption of the whole server system will be significantly affected by the non-energy proportion of DRAM. Furthermore, modern servers usually use NUMA architecture to replace the original SMP architecture to increase its memory bandwidth. It is of great significance to study the energy efficiency of these two different memory architectures. Therefore, in order to explore the power consumption characteristics of servers under memory-intensive workload, this paper evaluates the power consumption and performance of memory-intensive applications in different generations of real rack servers. Through analysis, we find that: (1) Workload intensity and concurrent execution threads affects server power consumption, but a fully utilized memory system may not necessarily bring good energy efficiency indicators. (2) Even if the memory system is not fully utilized, the memory capacity of each processor core has a significant impact on application performance and server power consumption. (3) When running memory-intensive applications, memory utilization is not always a good indicator of server power consumption. (4) The reasonable use of the NUMA architecture will improve the memory energy efficiency significantly. The experimental results show that reasonable use of NUMA architecture can improve memory efficiency by 16% compared with SMP architecture, while unreasonable use of NUMA architecture reduces memory efficiency by 13%. The findings we present in this paper provide useful insights and guidance for system designers and data center operators to help them in energy-efficiency-aware job scheduling and energy conservation. [ABSTRACT FROM AUTHOR]

The degree of integration of IT devices and consumption of cooling energy are consistently increasing owing to developments in the data center industry. Hence, to ensure the smooth operation and fault prevention of IT devices, the energy consumption of cooling systems has increased, leading to active research on improvements in cooling system performance for reducing energy consumption. This study examines the reduction in cooling energy consumption using a simulation by applying chilled water control and a water-side economizer (WSE) system to enhance the cooling system efficiency. The simulation results showed that the energy consumption was reduced by 1.8% when the chilled water temperature was set to 11 °C in a conventional system and by up to 19.6% when WSE was also applied. Furthermore, when the changes in chilled water temperature were applied for efficient operation of WSE, the energy consumption was reduced by up to 30.1% compared to that in conventional energy systems. [ABSTRACT FROM AUTHOR]

Software-defined networks have become more common in data centers. The programmability of these networks is a great feature that allows innovation to be deployed fast, following the increasing number of new applications. This growth comes with a cost of more processing power and energy consumption. Many researchers have tackled this issue using existing routing techniques to dynamically adjust the network forwarding plane to save energy. On the control-plane, researchers have found algorithms for positioning the controller in a way to reduce the number of used links, thus reducing energy. These strategies reduce energy consumption at the expense of processing power of the controllers. This paper proposes a novel approach to energy efficiency focused on the network's control-plane, which is complementary to the many already existing data-plane solutions. It takes advantage of the parallel processing capabilities of modern off-the-shelf multicore processors to split the many tasks of the controller among the cores. By dividing the tasks among homogeneous cores, one can lower the frequency of operations, lowering the overall energy consumption while keeping the same quality of service level. We show that a multicore controller can use an off-the-shelf multicore processor to save energy while keeping the level of service. We performed experiments based on standard network measures, namely latency and throughput, and standard energy efficiency metrics for data centers such as the Communication Network Energy Efficiency (CNEE) metric. Higher energy efficiency is achieved by a parallel implementation of the controller and lowering each core's frequency of operation. In our experiments, we achieved a drop of 28% on processor energy use for a constant throughput scenario when comparing with the single-core approach. [ABSTRACT FROM AUTHOR]

As a large number of small-scale PV plants have been deployed in distribution systems, generation forecasting of such plants has recently been gaining interest. Because the PV power mainly depends on weather conditions, it is important to accurately collect weather data for relevant PV sites to enhance PV forecasting accuracy. However, small-scale PV plants do not often have their own measuring apparatus to get historical weather data, so they have used weather datasets from relatively nearby weather data centers (WDCs). Therefore, these small-scale PV plants have difficulty delivering robust and reliable forecasting accuracy because of inappropriate predicted weather data from a distance. In this paper, two weather data mixing models are proposed: (a) inverse distance weighting (IDW), and (b) inverse correlation weighting (ICW). These models acquire adequate mixed weather data for the day-ahead generation forecasting for small-scale PV plants. Furthermore, the mixed weather data are collected using the geographic distance between the PV site and WDCs, or correlation between the PV generation and weather variables from nearby WDCs. Interestingly, the proposed ICW model outperforms when WDCs are located distant from the PV plants, whereas IDW performs well with the closer WDCs. The forecasting performance of the proposed mixing models was compared with those of the existing weather data collection methods. [ABSTRACT FROM AUTHOR]

Simulation and experimental studies were conducted to investigate energy consumption, develop ECMs (Energy Conservation Measures), and analyze temperature increase under a power failure scenario for a research data center at Youngstown State University. Two ECMs were developed to improve energy consumption by analyzing the thermal performance of the data center: (1) increase the return temperature in air conditioning vents; (2) provide cold aisle containment with the set point temperature increase. A transient analysis was conducted under a cooling system failure scenario to predict the temperature variation over time. The results suggest that it takes 600 s to increase the server inlet temperature by 16.1 °C for the baseline model. In addition, in the ECM #2, the maximum temperature at the server inlet did not reach 40 °C under the air conditioning system failure scenario, which is the maximum operating temperature of the ASHRAE A3 envelop. [ABSTRACT FROM AUTHOR]

Evaporative systems are probably the oldest technology for thermal comfort. However, they are still an essential technology in the food industry, environments for thermal comfort, and even for cooling data centers. Standards have been improved to compare the energy efficiency of this type of equipment. Using AHRI concepts with temperature data from the 29 most populous cities in the world, an EvaCOP index was created from temperatures that are easier to simulate than current parameters. The index parameters were tested in a laboratory located in Curitiba (Brazil). EvaCOP values of 45.58 and 25.77 W/W were found in the calculation in two different simulated equipment and compared with the compression cycle systems that in the most efficient machines is around 6.29 W/W. [ABSTRACT FROM AUTHOR]

Modern data centers are playing a pivotal role in the global economic situation. Unlike high-quality source of waste heat, it is challenging to recover the decentralized and low-quality waste heat sourced from data centers due to numerous technological and economic hurdles. As such, it is of the utmost importance to explore possible pathways to maximize the energy efficiency of the data centers and to utilize their heat recovery. Absorption chiller systems are a promising technology for the recovery of waste heat at ultra-low temperatures. In fact, the low temperature heat discharged from data centers cannot be retrieved with conventional heat recovery systems. Therefore, the present study investigated feasibility of waste heat recovery from data centers using an absorption chiller system, with the ultimate goal of electrical energy production. To fulfill this objective, a techno-economic assessment of heat recovery using absorption chiller (AC) technique for the data centers with power consumption range of 4.5 to 13.5 MW is performed. The proposed AC system enables saving electricity for the value of 4,340,000 kWh/year and 13,025,000 kWh/year leading to an annual reduction of 3068 and 9208 tons CO2 equivalent of greenhouse gas (GHG) emissions, respectively. The results of this study suggest an optimum change in the design of the data center while reducing the payback period for the investors. [ABSTRACT FROM AUTHOR]

The energy consumption problem has become a bottleneck hindering further development of data centers. However, the heterogeneity of servers, hybrid cooling modes, and extra energy caused by system state transitions increases the complexity of the energy optimization problem. To deal with such challenges, in this paper, an Energy Aware Task Scheduling strategy (EATS) utilizing marginal cost and task classification method is proposed that cooperatively improves the energy efficiency of servers and cooling systems. An energy consumption model for servers, cooling systems, and state transition is developed, and the energy optimization problem in data centers is formulated. The concept of marginal cost is introduced to guide the task scheduling process. The task classification method is incorporated with the idea of marginal cost to further improve resource utilization and reduce the total energy consumption of data centers. Experiments are conducted using real-world traces, and energy reduction results are compared. Results show that EATS achieves more energy-savings of servers, cooling systems, state transition in comparison to the other two techniques under a various number of servers, cooling modules and task arrival intensities. It is validated that EATS is effective at reducing total energy consumption and improving the resource utilization of data centers. [ABSTRACT FROM AUTHOR]

The problem of high power consumption in data centers is becoming more and more prominent. In order to improve the energy efficiency of data centers, cooperatively optimizing the energy of IT systems and cooling systems has become an effective way. In this paper, a model-free deep reinforcement learning (DRL)-based joint optimization method MAD3C is developed to overcome the high-dimensional state and action space problems of the data center energy optimization. A hybrid AC-DDPG cooperative multi-agent framework is devised for the improvement of the cooperation between the IT and cooling systems for further energy efficiency improvement. In the framework, a scheduling baseline comparison method is presented to enhance the stability of the framework. Meanwhile, an adaptive score is designed for the architecture in consideration of multi-dimensional resources and resource utilization improvement. Experiments show that our proposed approach can effectively reduce energy for data centers through the cooperative optimization while guaranteeing training stability and improving resource utilization. [ABSTRACT FROM AUTHOR]

Thermal comfort, indoor air quality (IAQ), and energy use are closely related, even though these have different aspects with respect to building performance. We analyzed thermal comfort and IAQ using real-time multiple environmental data, which include indoor air temperature, relative humidity, carbon dioxide (CO2), and particulate matter (e.g., PM10 and PM2.5), as well as electricity use from an energy recovery ventilation (ERV) system for a childcare center. Thermal comfort frequency and time-series analyses were conducted in detail to thoroughly observe real-time thermal comfort and IAQ conditions with and without ERV operation, and to identify energy savings opportunities during occupied and unoccupied hours. The results show that the highest CO2 and PM10 concentrations were reduced by 51.4% and 29.5%, respectively, during the occupied hours when the ERV system was operating. However, it was also identified that comfort frequencies occurred during unoccupied hours and discomfort frequencies during occupied hours. By analyzing and communicating the three different types of real-time monitoring data, it is concluded that the ERV system should be controlled by considering not only IAQ (e.g., CO2 and PM2.5) but also thermal comfort and energy use to enhance indoor environmental quality and save energy based on real-time multiple monitoring data. [ABSTRACT FROM AUTHOR]