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The world's energy consumption shows an increasing trend. Unfortunately, it is still dominated by the use of fossil energy. This condition results in concerns that an energy crisis will occur due to running out of non-renewable energy and an environmental crisis of global warming. One of the technology sectors that consumes a lot of energy is computer servers and data centers. This technology sector is growing rapidly due to the increasingly complex computing and larger data. One of the main causes of inefficiency in this technology is a decrease in performance due to an increase in temperature. Therefore, effective and efficient technology is needed to overcome the increasing operating temperature of computer servers and data centers. One of the most prominent cooling technologies to solve this problem is immersion colling. This method has developed in various types with their respective advantages and disadvantages according to application needs. Therefore, review literature is needed to comprehensively discuss the development of immersion cooling technology from the past until now and projected developments in the future. In more detail, this paper comprehensively compiles the latest findings of immersion cooling technology which includes an overview of the cooling system, history, implementation, construction type, and advantages. [ABSTRACT FROM AUTHOR]

The escalating demand for computational power and data storage has led to a substantial increase in the number and scale of data centers (DCs) worldwide. Due to characteristics of high heat flux and reliable operational security, there is an urgent need for innovative solutions to solve the problem of DC cooling and waste heat recovery. Direct liquid cooling technology is one of the most promising energy-saving cooling technologies due to its advantages of high cooling efficiency, low noise, and reduction of hot spots. Waste heat recovery is also one of the effective ways to improve energy efficiency and reduce carbon emissions due to higher coolant temperature. This study provides a comprehensive review of direct liquid cooling and waste heat recovery technologies for DCs that have been published in high level journals in recent years. Herein, the primary innovation and contribution of this review paper is its comprehensive coverage of advanced/emerging cooling technologies for DCs including immersion cooling and spray cooling, and its broad overview of global trends in performance, energy consumption, power density, and PUE of related systems. In addition, this review addresses the issue of heat recovery and reuse in DCs with an overview of common waste heat recovery technologies such as heating, district heating networks, cooling supply and organic Rankine cycle (ORC) are summarized and then compared and analyzed their application scenarios, advantages and disadvantages. Through the comprehensive review of cutting-edge technologies and methodologies for direct liquid cooling and waste heat recovery, this review aims to provide a thorough understanding of current landscape and future direction of sustainable and high-performance DC solutions. [Display omitted] • The systematic research and analysis of direct cooling and waste heat recovery are elaborated. • Simulation, and experimental studies of immersion cooling and spray cooling are summarized. • Comparison of energy saving potential and PUE of DC direct liquid cooling are presented. • Regard various applications to conduct a comprehensive review of waste heat recovery in DCs. • The existing challenges and future work opportunities are discussed. [ABSTRACT FROM AUTHOR]

Advancements in data center high performance cooling. [Display omitted] • The development of predominant air supply method in data centers was analyzed. • The supply/return water temperatures and large temperature difference technologies were discussed. • The components and configurations of data center cooling systems were studied. • Different intelligent management strategies of cooling systems were compared. To expedite the achievement of energy conservation and consumption reduction in data centers, it is essential to not only utilize ambient energy effectively and maximally but also to enhance the energy efficiency of mechanical refrigeration. The synergy between free cooling and mechanical refrigeration plays a crucial role in reducing the power usage effectiveness (PUE) of data centers. This, in turn, maximizes the energy efficiency of the cooling systems, ensuring their safe, stable, and efficient operation. This paper synthesizes research on various aspects of cooling systems for data centers, including air distribution, supply and return water temperature, system components and configurations, and intelligent management. The analysis provides a vital reference for the development of environmentally friendly and efficient cooling systems in data centers. [ABSTRACT FROM AUTHOR]

• Criteria for assessing the applicability of air-side economizers were proposed, encompassing energy-saving potential and corrosion risk. • The consistency between available hours with different counting methods and energy-saving potential was compared. • When adopting isenthalpic humidification, humidification energy consumption accounts for less than 6% of the cooling energy consumption, so not considering the lower limit of DPT has little impact on the assessment of the energy-saving potential. • More reasonable indicators and indicator values were screened for applicability assessment and climate zoning. The application effect of air-side economizer is closely related to outdoor air temperature, humidity and cleanliness, leading to significant variations in different regions. In this paper, the key factors affecting the applicability of air-side economizers were analyzed in terms of energy-saving potential and corrosion risk, and their assessment criteria were screened out. Then the K-mean cluster analysis was performed on zoning indicators from assessment criteria, and two zoning results were obtained, both of which could divide the country into eight climate zones. To determine a more reasonable zoning result, the consistency between available hours with different counting methods and energy-saving potential, as well as the effect of humidification energy consumption on energy-saving potential was discussed through energy consumption analysis. We conclude that the humidification method has a significant impact on the energy consumption and zoning results of the air-side economizer. When adopting isenthalpic humidification, it is more appropriate to assess the energy-saving potential of the air-side economizer by the annual available hours with DBT out ≤ 27 °C and DPT out ≤ 15 °C. Additionally, the zoning result obtained with this and pollutant concentration indicators is more reasonable. In the zoning results, Zone I is most suitable for applying air-side economizers due to its large energy-saving potential and low corrosion risk, but only includes Lhasa. However, Zone VII and Zone VIII are deemed to have high corrosion risk, resulting in higher costs for risk control in these areas, which comprise Taiyuan, Urumqi, Xi'an, Zhengzhou, Tianjin, Jinan and Shijiazhuang. [ABSTRACT FROM AUTHOR]

The growing demand for cloud computing and storage necessitates the expansion of Data Centers, thereby increasing energy consumption and environmental footprint. The data-driven dynamic thermal control presents a promising solution to mitigate this issue, as it can offer improved thermal control strategies and address the intricate and stochastic nature of the thermal environment. Nonetheless, existing solutions typically exhibit deficiencies in terms of reliability and practicability. To this end, this paper proposes a feasible uncertainty-aware online learning method with reliable dynamics models and controllers. Specifically, Deep Learning models with the capacity for uncertainty quantification are employed to account for the imperfections in pre-trained models, hence providing reliable predictions. The Cross-Entropy Method and Monte Carlo trajectory sampling solve optimization problems while considering uncertainties. It is mathematically analyzed that the uncertainty quantified by the system dynamics model can be captured with sufficient trajectory samples, rendering a reliable controller. A practical framework is proposed and tested on a widely used Data Center model to mitigate the insufficient capacity of Deep Learning models to quantify uncertainties. The results demonstrate its superior performance during the initial training stage, achieving a 66% reduction in violations and 3.76% power savings compared to the default controller. Continuous active online learning enhances its adaptability, resulting in an 80% reduction in violations and a 6% energy saving. The thermal control strategies are thus improved by the proposed new solutions and insights. [ABSTRACT FROM AUTHOR]

Highlights • Realize IT load demand response controllability with data center resource scheduling. • Design server cluster workload scheduling method with time shift mechanism. • Design data center UPS energy storage dynamic response mechanism. • Holistic dynamic response method smoothens data center tie-line power fluctuation. Abstract With the rapid development of cloud computing, artificial intelligence technologies and big data applications, data centers have become widely deployed. High density IT equipment in data centers consumes a lot of electrical power, and makes data center a hungry monster of energy consumption. To solve this problem, renewable energy is increasingly integrated into data center power provisioning systems. Compared to the traditional power supply methods, renewable energy has its unique characteristics, such as intermittency and randomness. When renewable energy supplies power to the data center industrial park, this kind of power supply not only has negative effects on the normal operation of precision equipment, such as CPU/GPU chips and hard disk, in data center, but it would also impact the stability of the utility power grids operation. To solve this problem, this paper presents a novel tie-line power fluctuation smoothing algorithm with consideration of data center's holistic demand response. The contributions of this paper are: (1) overcoming the limitations of treating IT load as uncontrollable workload in the traditional demand response research, we design a data center resource scheduling model to realize IT load demand response controllability; (2) two novel mechanisms are proposed: (i) the server cluster workload scheduling method with time shift mechanism, and (ii) the data center UPS (Uninterruptible Power Supply) energy storage dynamic response mechanism. (3) Combining these two mechanisms as holistic demand response of data center, we present a tie-line power fluctuation smoothing algorithm to improve power supply reliability, which is beneficial to both the high density and precision IT equipment in the data center and the utility power grid. In the experiments, the results show that the new algorithm can effectively regulate the tie-line power fluctuations under different server cluster utilization ranges and scenarios of large-scale penetration of distributed renewable energy scenarios. The new algorithm is hence able to contribute beneficially to the reliability and stability of intelligent industrial park micro-grid and utility power grids. [ABSTRACT FROM AUTHOR]

Highlights • Data centers have a lot of excess capacity due to their redundant design. • This capacity can be used to provide ancillary services, such as primary regulation. • UPS systems are technically and economically viable in primary regulation. • Additional stress to battery systems is within battery specifications. • Participation to primary regulation can create significant revenue for data centers. Abstract Data centers have a significant potential to become a major source of flexibility in smart girds. They consume currently roughly 3% of all the electricity produced globally and are expected to only increase their consumption as the world becomes more connected and digitalized. As data centers are required to operate without any interruptions, they use power protection systems and energy storages. This paper investigates the technical and economic feasibility of dual-purposing these power protection systems, the uninterruptible power supplies, and their batteries in data centers to perform primary frequency regulation services. While the topic of data centers and demand response has been extensively covered in the current scientific literature, the focus has been on the demand response enabled by server workload shifting or hardware-enabled peak shaving. Based on an extensive literature review, there is a knowledge gap in the literature concerning primary frequency regulation and dynamic response enabled by modern power electronics systems in data centers. In this paper, this knowledge gap is bridged by suggesting a novel approach of taking advantage of the bidirectional operations capabilities of the uninterruptible power supply systems, thereby enabling them to provide dynamic power response from their battery systems. The feasibility of this approach is examined with the proposed method, which includes (1) an analysis of the required energy for primary regulation and the availability of this energy in a typical data center, (2) a simulation of activation events and their impact on the service life of the battery systems, (3) reaction speed and reliability considerations of the operations, and (4) an economic feasibility and balancing market analysis. The results show that as primary frequency regulation is an energy nonintensive service and data center battery systems are by design oversized for redundancy reasons, typical data centers have more than ample amounts of energy to participate in the primary regulation without jeopardizing their own processes. The results also show that by maintaining reasonable levels of usage, the battery systems can be operated within their specifications, and the demand response operations will not cause premature aging of the battery systems. The reaction speed of the power electronics is found to be very high and easily meet the current market requirements. While the achievable revenue from the primary regulation service is small compared for example with the electricity costs of the data centers, it is still significant as there is little to no impact on the daily business of the data centers. [ABSTRACT FROM AUTHOR]

The energy conservation for cooling system has attracted widespread interest in a data center design and operation. Black box assumption is a conventional simplification approach for a rack. However, there is no complete description about how to deal with the boundary conditions for the rack model in the existing literatures. In this paper, the boundary conditions and their numerical treatments for the conventional black box model of a rack are clearly presented, seeming firstly in the literature, and realized by self-coding. The developed model is validated compared with the simulation results using ANSYS Icepak for a typical raised floor data center and good agreements are observed. Furthermore, the conventional black box model in ANSYS Icepak is improved in two aspects of (1) the determination method of server flow rate , (2) the determination of the temperatures of the rack outlet surfaces by point-point correspondence method. Under the studied condition, the conventional black box model will underestimate the maximum temperature in the studied data center by 6.21 °C, and the mixing length zero equation turbulence model shows 2–3 orders of magnitude of time saving for computation and higher convergence and numerical stability compared with the standard k - ε model with an acceptable accuracy. • Black box model treatments in data center modeling are comprehensively clarified. • Good agreement between the simulated results and those by using Icepak. • Non-isothermal treatment should be used for rack outlet surface. [ABSTRACT FROM AUTHOR]

Regional data center clusters have flourished in recent years to serve customers in a major city with low latency. The optimal coordination of data centers in a regional cluster has become a pressing issue because of their rising energy consumptions. In this paper, an online algorithm based on Lyapunov optimization framework is developed for the combined computing workload and energy coordination of data centers in a regional cluster. The proposed online algorithm is prediction-free and easy to implement. We prove that the workload queues and battery energy level will be within their physical limits, though their related time-coupling constraints are not considered explicitly in the proposed algorithm. The previous online algorithms do not have such a guarantee. A theoretical upper bound on the optimality gap between the online and offline results is derived to provide a performance guarantee for the proposed algorithm. To enable distributed implementation, an accelerated distributed coordination algorithm is developed based on the alternating direction method of multipliers (ADMM) with iteration truncation and follow-up well-designed adjustments, whereby a nearly optimal solution is attained with much enhanced computational efficiency. Case studies show that the proposed algorithm reduces the operational costs and saves computation time compared to online benchmarks. • The combined computing workload and energy coordination of data centers is studied. • A prediction-free online coordination algorithm is built with proven properties. • An accelerated ADMM algorithm is developed for distributed implementation. • The proposed coordination method greatly reduces the total cost of data centers. • The computation time is largely reduced compared to the traditional ADMM. [ABSTRACT FROM AUTHOR]

With the rapid development of global informatization, data center computing tasks are increasing, and the resulting energy consumption problems are particularly prominent in the context of carbon emission reduction, and affect the safe and green operation of data centers. Aiming at the problem of high and large fluctuation energy load of data center servers, this paper presents a method of optimizing data center servers load considering spatial-temporal load distribution of computing resources. Co-optimized allocation of computing resources and load is studied with examples from the property that computing resources is adjustable in time and space dimensions. The results show that the proposed method is able to reduce the peak electric and cooling loads of the data center room by 10.7 %, reduce the total annual electric consumption by 2.66 % and the total annual cooling consumption by 2.46 %, and reduce the economic and environmental costs of energy supply for the data center in the case study, which comprehensively shows that the load optimization of data center servers has been achieved through the optimal allocation of computing resources. [ABSTRACT FROM AUTHOR]

In order to maintain a stable thermal environment of the data center, air-conditioning systems for the data center are in high energy consumption operation state yearly. Airflow organization in a data center impacts the cooling effectiveness of the air-conditioning systems significantly. This paper investigated a small-scale data center by combine of measurement and CFD simulation. To supply accurate boundary conditions, site measurement was conducted to validate the simulation results. In this paper, three optimization approaches of airflow organization were proposed and simulated by CFD software. The cooling performance of these approaches were compared by the temperature of 16 points on the same plane (Z=1.5m) in the room. The results indicated that cold aisle closure could eliminate by-pass airflow but also worsen thermal environment. Setting air return on both sides of hot aisle got most significant improvement in cooling performance. The temperature of hot points could be decreased by 0.5~5°C. [ABSTRACT FROM AUTHOR]

Cloud computing has brought about a transformation in the delivery model of information technology from a product to a service. It has enabled the availability of various software, platforms and infrastructural resources as scalable services on demand over the internet. However, the performance of cloud computing services is hampered due to their inherent vulnerability to failures owing to the scale at which they operate. It is possible to utilize cloud computing services to their maximum potential only if the performance related issues of reliability, availability, and throughput are handled effectively by cloud service providers. Therefore, fault tolerance becomes a critical requirement for achieving high performance in cloud computing. This paper presents a comprehensive overview of fault tolerance-related issues in cloud computing; emphasizing upon the significant concepts, architectural details, and the state-of-art techniques and methods. The objective is to provide insights into the existing fault tolerance approaches as well as challenges yet required to be overcome. The survey enumerates a few promising techniques that may be used for efficient solutions and also, identifies important research directions in this area. [ABSTRACT FROM AUTHOR]

• Algorithm can be sensitive to algorithm setting, affecting optimality and robustness. • The discrepancy between the digitized and initial objectives cannot be ignored. • The system dynamics that can affect potential performance improvement is revealed. • Algorithms can obtain energy savings and violation reductions in some scenarios. • Actor-critic, off-policy, and model-based algorithms exhibit better performance. Deep Reinforcement Learning has been increasingly researched for Dynamic Thermal Management in Data Centers. However, existing works typically evaluate the performance of algorithms on a specific task, utilizing models or data trajectories without discussing in detail their implementation feasibility and their ability to deal with diverse work scenarios. The lack of these works limits the real-world deployment of Deep Reinforcement Learning. To this end, this paper comprehensively evaluates the strengths and limitations of state-of-the-art algorithms by conducting analytical and numerical studies. The analysis is conducted in four dimensions: algorithms, tasks, system dynamics, and knowledge transfer. As an inherent property, the sensitivity to algorithms settings is first evaluated in a simulated data center model. The ability to deal with various tasks and the sensitivity to reward functions are subsequently studied. The trade-off between constraints and power savings is identified by conducting ablation experiments. Next, the performance under different work scenarios is investigated, including various equipment, workload schedules, locations, and power densities. Finally, the transferability of algorithms across tasks and scenarios is also evaluated. The results show that actor-critic, off-policy, and model-based algorithms outperform others in optimality, robustness, and transferability. They can reduce violations and achieve around 8.84% power savings in some scenarios compared to the default controller. However, deploying these algorithms in real-world systems is challenging since they are sensitive to specific hyperparameters, reward functions, and work scenarios. Constraint violations and sample efficiency are some aspects that are still unsatisfactory. This paper presents our well-structured investigations, new findings, and challenges when deploying deep reinforcement learning in Data Centers. [ABSTRACT FROM AUTHOR]

As a critical infrastructure to support the development of digital economy, the scale of data center (DC) power consumption has been growing continuously in recent years. The plug-in of a large number of DCs to the distribution network (DN) presents both opportunities and challenges for the secure, stable, and sustainable operation of the system. In order to address the planning issues arising from the integration of DCs into the DN, this paper proposes a two-stage (planning stage and operation stage) collaborative planning method for DCs and the DN while considering the source-demand uncertainty. Firstly, spatial-temporal demand response characteristic of DCs is explicitly elaborated, and based on the energy consumption model, a two-stage stochastic planning model considering conditional value at risk (CVaR) for DCs and the DN is constructed using probabilistic stochastic simulation, which obeys low-carbon and security, and satisfies the source-network-load constraints of the system. The siting and sizing for DCs and renewable generation, feeder expansion for the DN are determined to minimize the investment cost during the planning stage, while the power output for the devices considering uncertainties from load and renewable energy are optimized to minimize the operation cost during the operation stage. Secondly, the L-shaped decomposition algorithm is proposed to solve the two-stage stochastic optimization problem by reconstructing the problem into a master problem and subproblems, where the dual multiplies are calculated to generate feasibility cuts and optimality cuts to achieve iterative solving. Finally, numerical case studies on a 33-bus distribution system demonstrate that the participation of DCs in demand response can achieve a significant reduction in the investment and operation cost of the DN, and further promote the consumption of distributed renewable energy sources. • The spatial-temporal characteristics of DC demand response is explicitly elaborated and the mathematical formulation is proposed. • A two-stage stochastic collaborative planning approach of DC and DN based on CVaR is proposed. • Guidance for investment decisions for DSO with large numbers of DCs plug-in under uncertainties. • Demand response of DCs can effectively support renewable energy consumption and reduce the cost of DN. [ABSTRACT FROM AUTHOR]

In the age of digitalization, data centers are critical facilities that help people and businesses in many sectors. Data centers, like other assets requiring high investment, face several risks during their operation. Also, data centers seek sustainable sources to boost their operations when selecting the most appropriate location. Therefore, the location of a data center is chosen based on sustainability and risk assessment. To the best of our knowledge, no study has been conducted for data center risk evaluation with environmental and sustainability assessment. This paper evaluates the eight main criteria and the forty-five sub-criteria for selecting the most appropriate location for the data center when considering the fifty different economies in different business regions. For this particular purpose, the continuous intuitionistic fuzzy sets (CIFS)-based analytical hierarchy process (AHP) is presented to define the weights of each criterion and sub-criterion. Then, the CIFS-based technique for order of preference by similarity to the ideal solution (TOPSIS) is developed to rank the fifty economies integrated with the weights from the CIFS-based AHP. Moreover, eight different scenarios were conducted, and comparison studies were presented. Finally, Denmark, Switzerland, and The Netherlands are the top three selections based on the criteria. [Display omitted] • Fuzzy multi-criteria methods are developed to evaluate data center locations. • Natural, energy, and infrastructure risks are the top three pillars. • Economies are ranked based on the eight scenarios with sustainability and risks. • Denmark tops the charts with the highest score associated with fifty economies. • Based on each income level, Denmark, Brazil, and Tunisia are suitable locations. [ABSTRACT FROM AUTHOR]

A full-knowledge of the temperature distribution in a data center is crucial for the design and operation to avoid thermal failure. However, it is a great challenge to conduct full-field simulations using the computational fluid dynamics/heat transfer (CFD/HT) method due to the unacceptable computational cost caused by the multi-scale structure of data centers. In this paper, a novel multi-scale model based on POD and CFD coupling is proposed to predict the thermal and fluid characteristics in data centers from room scale to chip scale. The temperature and flow fields in an air-cooled data center are predicted and analyzed under the scales of room, server, IGBT and chip. Based on the CFD results of Room-Server level, 16 sets of design cases' temperature fields for Server-IGBT level are compared using the POD and CFD methods, which shows that the maximum average absolute deviation and average relative deviation are 0.004 °C and 0.59%, respectively. Subsequently, three sets of off-design cases for Server-IGBT level and IGBT-Chip level are predicted using the aforementioned methods, yielding the maximum relative deviation 7.21% and 0.98%, respectively. The salient outcome is the remarkable 175-fold reduction in CPU processing time achieved with the POD model compared to traditional CFD simulations. [ABSTRACT FROM AUTHOR]

There are various shortages of the traditional data center in cost, resource utilization, power consumption and operation. This paper studies the development trend of the virtual data center and its technical advantages, proposes a scheme of virtual system of smart transportation data center based on VMware vSphere. At the same time, oriented on requirements of traffic data stream management in city smart transportation system under the environment of cloud computing, aimed at massive, multi-source, real-time, dynamic uncertain data stream sent back from all kinds of cross regional intensive control perception device, this paper analyses the characteristics and correlation of actual city traffic operation and traffic data flow, researches evolution mechanism of uncertain data; construct traffic data flow model based on ontology, core metadata and theory of constraints. And on this basis, this paper uses virtualization and large data sets of parallel processing, considers load balancing and adaptive mechanism, combines fuzzy theory and dynamic multi object, multi constrained decision theory, to seek the efficient query algorithm for dynamic, complex and continuous transportation data stream. [ABSTRACT FROM AUTHOR]

This paper aims at identifying and presenting tools and techniques to help perform IT database audit. The study summarizes the issues relating to the national and international IT audit regulations. It highlights the key differences between audit and other forms of economic audit (e.g. financial, internal or statutory). The paper presents the main components of IT audit, namely data center audit, computer network audit, operating system audit, Internet servers audit, database auditing, application auditing, external access flows IT audit (e.g. radio access, access from mobile devices). Practical aspects of the study describe database auditing as an important element of IT audit process. Current trends are presented for processing and storing data in relationship with data storage centers, cloud processing and inclusion of mobile devices as data access terminals. The author makes a presentation of database components that are relevant to database audit. Database audited components concern: file system database, data dictionary, database objects, database customer analysis, data protection and backup system, security and data protection from database. The last part of the paper is dedicated to preparing a new practical approach regarding the audit steps and technical means used in databes auditing. Finally, the author emphasizes the importance of database auditing, of data security and of the need to develop new technical auditing methods which are to be in consensus with the rapid development of Information Technology. [ABSTRACT FROM AUTHOR]

Cloud Computing has emerged as a new paradigm that permits to deliver Information Technology resources on demand and outsource software and hardware infrastructure. To support dynamic load, the system should select the best location for serving the request to achieve best performance. Selection of data center plays an important role to improve the performance as well as to reduce the cost. Service broker controls the traffic routing between user bases and data centers based on different service broker policies. In this paper we propose a new service broker algorithm which combines the cost and efficiency for optimized the cost and response time. [ABSTRACT FROM AUTHOR]

The waste heat recovery technology of data center has attracted the attentions for energy conservation and emission reduction in recent years. This paper presents a new scheme which uses heat recovery technology to link the data center and the agricultural sector. The detailed system design, technical validation, and environmental and economic analysis show that the project can fully absorb a large amount of waste heat in the data center, and use the air source heat pump (ASHP) to transport heat to an ecological farm, reducing the consumption of electric energy and promoting the development of agriculture and animal husbandry. In a case study of China Brain Green Data Center, the dynamic payback period is about 3.4 years, and the cumulative net present value will reach $666 k. In terms of environmental impact, the proposed ASHP-based complementary utilization scheme can save 676 MWh of electricity for the data center and 88 MWh of electricity for the ecological farm in each year, which is equivalent to reducing coal by 230 tons and reducing CO 2 emissions by 168 tons. Therefore, from the perspective of economic and environmental benefits, this new complementary waste heat utilization technology realizes a win-win development. [ABSTRACT FROM AUTHOR]

Recently, waste heat recovery has enabled data centers to serve as energy prosumers with flexible features that play essential roles in the coordination operation of integrated electricity–heat systems (IEHSs). The energy hub, as a critical component of IEHSs, provides a promising opportunity to improve energy efficiency. In this paper, the decentralized coordination optimization for IEHSs with data centers is explored to protect the confidential information of different entities. First, a novel data center based energy hub (DCEH) model is developed, where energy conversion, consumption and storage present high flexibility. Especially, in addition to heat recovery, several other controllable operational characteristics of a single data center are considered, involving servers, workloads and indoor temperature. Then, the coordination optimization model of IEHSs with DCEH (IEHSs-DCEH) is constructed by incorporating energy consumption cost and carbon emission, where the energy networks of IEHSs are considered. Moreover, a learning-aided relaxed alternating direction method of multipliers (LR-ADMM) algorithm is proposed to solve the dispatching model of IEHSs-DCEH considering communication packet loss. The proposed LR-ADMM algorithm embeds a momentum extrapolation based prediction technique, which can obtain the predicted value of missing boundary information without adding computational burden, even in continuous packet losses. Simulation results demonstrate that the developed coordination dispatching model can achieve higher economic and environmental benefits than the reference one that ignores data centers' flexibility. Additionally, the proposed LR-ADMM algorithm with proper prediction factors exhibits a faster convergence rate and robustness hedging against packet losses compared to the ADMM and relaxed ADMM approaches. • A highly flexible data center based energy hub model is developed. • Impacts of the flexibility in the data center based energy hub are investigated. • The coordination of integrated energy systems with data centers is optimized. • A learning-aided decentralized algorithm is proposed hedging against packet loss. [ABSTRACT FROM AUTHOR]

Through large scale deployment of cloud data centers, burning up of energy by data center is very high and violations of service level agreements (SLA) are increasing. Implementation of Virtual machine consolidation techniques direct to physical host over exploitation which degrades the performance of physical host and Virtual machine. It has been a momentous confronts to get better exploitation of resources and energy effectiveness in cloud data center. This paper proposed knowledge based reduction method for Virtual machine assignment and consolidation. This method chooses the Virtual machines from the overloaded host and migrate the Virtual machines to other physical host to diminish energy burning up and get better quality of service. To provide evidence and effectiveness of proposed method, the experiment is conducted with diverse workload circumstances of real system. The experimental consequences exhibits that the proposed method diminishes energy burning up and optimizes infringement of service level agreements in achieving better performance. [ABSTRACT FROM AUTHOR]

• Equation-based object-oriented Modelica models for data center cooling systems are built and released in the open-source Modelica Buildings library. • Together with the Electrical package in the Modelica Buildings library, the introduced package can simulate the thermal dynamics as well as the electrical dynamics for conventional and renewable data centers. • New strategies toward emergency operations for the data center cooled by chillers and waterside economizers are investigated. In this paper, we introduce a newly developed open source data center package in the Modelica Buildings library to support modeling and simulation of cooling and control systems of data centers. The data center package contains major thermal and control component models, such as Computer Room Air Handler, Computer Room Air Conditioner, models of different subsystem configurations such as chillers with differently configured waterside economizers, as well as templates for different systems. Two case studies based on this package are performed to investigate the performances of the cooling and electrical system under normal conditions and emergency situations such as a blackout: one is for a data center powered by conventional energy, and the other is for a data center powered by renewable energy. Simulation results show that the dynamic modeling and multi-domain simulation in the Modelica-based tool make it convenient for users to investigate both normal and emergent operations for conventional and renewable data centers. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of reducing energy consumption within high-performance computing data centers, especially for those with a large portion of "small size" jobs. Different from previous works, the efficiency of job scheduling and processing is made as the first priority. To reduce energy from servers while maintaining the processing efficiency of jobs, a new hysteresis computing resource-provisioning algorithm is proposed to adjust the total computing resource reactively. A dynamical thermal model is presented to reflect the relationship between the computational system and cooling system. The proposed model is used to formulate constrained optimal control problems to minimize the energy consumption of the cooling system. Then, a two-step solution is proposed. Firstly, a thermal-aware resource allocation optimizer is developed to decide where the resource should be increased or decreased. Secondly, an economic model predictive controller is designed to adjust the cooling temperature predictively along with the variation of the rack power. Performance of the proposed method is studied through simulations with real job trace. The results show that significant energy saving can be achieved with guaranteed service quality. • On the basis of scheduling and processing jobs efficiently, a control framework is proposed for HPC data centers to coordinate hysteresis resource provisioning, thermal-aware allocation and dynamic cooling management. • An economic model predictive control based technique for the modeling and managing the thermal environment. • Comparison of proposed method is made with existing techniques. The proposed methodology achieves significant energy saving, and low performance loss in service quality. [ABSTRACT FROM AUTHOR]

Cloud Computing has emerged as a new paradigm that permits to deliver Information Technology resources on demand and outsource software and hardware infrastructure. To support dynamic load, the system should select the best location for serving the request to achieve best performance. Selection of data center plays an important role to improve the performance as well as to reduce the cost. Service broker controls the traffic routing between user bases and data centers based on different service broker policies. In this paper we propose a new service broker algorithm which combines the cost and efficiency for optimized the cost and response time. [ABSTRACT FROM AUTHOR]

Abstract The increasing amount of renewable energy in power systems poses challenges for the system operators to handle the volatility of power generation. Demand response and lithium-ion (Li-ion) based battery systems have been suggested as a promising solution to provide balancing services to address this challenge. In the paper we investigate the economic feasibility of providing primary regulation services with dual-purposed Li-ion batteries in the power protection systems of data centers. This is also compared with investments in dedicated battery energy storages. Our analysis shows that investments in dual-purpose data center batteries have a higher profitability, even if this means that the battery capacities in the power protection systems have to be oversized with respect to the data center backup time requirements. [ABSTRACT FROM AUTHOR]

Abstract Hybrid cooling system has become a promising thermal management system for data center considering its capability of removing heat from various electronic components systematically. Water cooling and air cooling can be used for cooling the active component and passive component respectively. A novel triple fluid heat exchanger (TFHEX) is designed to dissipate the heat from these fluids into the coolant. This paper discusses thermal and hydrodynamic performance of TFHEX that specially designed for hybrid cooling of data. In present work, the THFEX is designed to dissipate heat from 32kW rack in which the server that dissipates 90% of heat from the active component and 10% of from passive component. Since the water side has much higher load, heat transfer from tube to tube in TFHEX has to be carefully maintained without significant increase in pressure drop. It was found that TFHEX can dissipate heat from different heat source at various load without significant increase in the pressure drop if designed carefully and therefore this heat exchanger can be a feasible solution for hybrid cooling data center. The results critically evaluated the trade-off between the heat transfer capacity of TFHEX and its pressure drop i.e. the pumping power requirement. This can be a reference for data center engineers to select the appropriate configuration for TFHEX for hybrid cooling. [ABSTRACT FROM AUTHOR]

Highlights • Experimental tests of five IEC configurations are carried out. • Performance significantly depends on system configurations and water flow rate. • Highest effectiveness occurs in case of horizontal orientation of plates. • Lowest effectiveness occurs with water and secondary air supplied from the bottom. Abstract The indirect evaporative cooler (IEC) can be effectively used to increase energy efficiency and reduce energy consumption of HVAC systems. In particular, such system can be suitably adopted in data center applications, where the design indoor temperature can reach 45 °C, according to recent ASHRAE standards. It is well known that IEC performance can be significantly increased by an effective secondary air humidification process. In particular, the system should be designed in order to obtain an optimal water distribution on heat exchanger plates. In this paper, five different water nozzles and airflows arrangements of a IEC system based on a cross flow heat exchanger are experimentally investigated. The system is designed to have a limited water consumption (water to secondary air mass flow ratio between 1.4%–4%) and performance are measured in different operating conditions. Results show the highest wet bulb effectiveness (up to 82%–84% in the investigated conditions) is obtained in two configurations: (i) Horizontal orientation of heat exchanger plates; (ii) Vertical orientation of heat exchanger plates and water/secondary air supplied from the top. [ABSTRACT FROM AUTHOR]

Abstract: The Cloud computing becomes an innovative computing paradigm, which aims to provide reliable, customized and QoS guaranteed computing infrastructures for users. This paper presents our early experience of Cloud computing based on the Cumulus project for compute centers. In this paper, we give the Cloud computing definition and Cloud computing functionalities. This paper also introduces the Cumulus project with its various aspects, such as design pattern, infrastructure, and middleware. This paper delivers the state-of-the-art for Cloud computing with theoretical definition and practical experience. [Copyright &y& Elsevier]

Cloud computing and data centers that support this paradigm are rapidly evolving in order to satisfy new demands. These ever-growing needs represent an energy-related challenge to achieve sustainability and cost reduction. In this paper, we define an expert and intelligent system that applies various energy policies. These policies are employed to maximize the energy-efficiency of data-center resources by simulating a realistic environment and heterogeneous workload in a trustworthy tool. An environmental and economic impact of around 20% of energy consumption can be saved in high-utilization scenarios without exerting any noticeable impact on data-center performance if an adequate policy is applied. [ABSTRACT FROM AUTHOR]

Highlights • The art on airflow distribution and management in data center is reviewed. • Experimental and numerical investigations of several typical cases are summed. • Energy efficiency of air distribution must be based on the reliability of data center. • HACS is a promising air management system due to its flexibility and extendability. • Improvement methods of vertical airflow uniformity needs to be developed. Abstract Rapid expansions of high-density data centers have been triggered worldwide owing to the high demand of cloud computing over the past decade. The airflow distribution and management have great association with the thermal performance of the typical underfloor air distribution (UFAD) data center. Therefore, numbers of configurations and methods have been proposed to improve the thermal performance of data centers with various operation conditions. This paper reviews the ventilation configurations of data center and presents the geometric effects of underfloor plenum on airflow. The experimental and numerical investigations on typical cases as well as the relevant parameters are also summed up. Various air management methods including cold aisle containment system(CACS), hot aisle containment system(HACS) and vertical exhausted duct system(VEDS) are analyzed on the benefits and limitations. Based on the enormous amount of research on the traditional UFAD system with hot aisle/cold aisle (HA/CA) arrangement, it can be concluded that the practices of data centers have a tendency of being more flexible and extendable, while the literature on the corresponding cases which applied HACS or other air distribution systems remains scarce. Moreover, beside the improvement of the airflow uniformity along the cold aisle, methods for enhancing the airflow uniformity along the height direction are also encouraged to be developed. [ABSTRACT FROM AUTHOR]

• The construction of the enhanced plate used in the AHU is proposed. • The highest enhanced effects are obtained for "H12-R10-h4" (PEC=1.45 at Re=5000). • All nine enhanced channels can enhance heat transfer at the same pressure drop. • The DMTD is proposed for the cross-flow AHU heat exchanger design. • The heat exchanger picked up is "H7.5-R10-h3" which has the maximum ratio of the heat exchange rate (40.5 kW) over the flow loss power (3.25 kW). In this paper, the construction process of the enhanced plate, the heat transfer and fluid flow characteristics, and the domain decomposition method for AHU performance estimation are presented. The AHU heat exchanger 3D model is built from plate modeling to heat exchanger assembling. The heat transfer plate modeling starts from a basic flat plate, elliptic cylindrical dimples (ECDs) are firstly added to the fixed location, and then spherical crown dimples (SCDs) are applied to the flat surface among the ECDs. For the enhanced channels, the effect of channel height is weaker than SCD depth on the Nusselt number and friction factors. For all enhanced channels, the PECs are larger than 1.1 within Re from 5000 to 35,355. The performance of a deeper depth of SCD is superior to that of a shallow one when the channel height is the same. Among the nine channels studied, the highest enhanced effects are obtained for "H12-R10-h4" (PEC=1.45 at Re=5000). And all nine enhanced channels can enhance heat transfer at the same pressure drop among the Reynolds number of 5000 to 35,355. The energy saving performance decreases with Re, and the channels with smaller SCD depth can save more energy when channel height is identical at the higher Reynolds number region. A decomposition thermal design method is proposed for the cross-flow AHU heat exchanger design, and the typical AHU design result is picked based on the heat exchange rate and flow loss power. The recommended heat exchanger is "H7.5-R10-h3" whose heat exchange rate is 40.5 kW when flow loss power is 3.25 kW. [ABSTRACT FROM AUTHOR]

Energy efficiency is a critical issue in the management and operation of data centers, which form the backbone of cloud computing. Virtual machine placement has a significant impact on the energy efficiency of virtualized data centers. Among various methods to solve the virtual-machine placement problem, the genetic algorithm has been well accepted for its quality of solution. However, it is computationally demanding largely due to the complex form of fitness function, limiting its applications in data centers. To enhance the computational efficiency of the genetic algorithm while maintaining its quality of solution, a progressive-fidelity approach is developed in this paper for genetic-algorithm computation. It starts with a low-fidelity genetic algorithm with a simple fitness function. Then, for solution refinement, it switches to a medium-fidelity genetic algorithm with a more complicated fitness function. Finally, it progresses to the fine tuning of solution through a high-fidelity genetic algorithm with the energy consumption of data centers as fitness function. Heuristics are presented for the adaptive switching of genetic-algorithm computation from low fidelity to medium fidelity and finally to high fidelity. Experiments show that compared with the standard genetic algorithm of high fidelity, our progressive-fidelity approach of genetic algorithm computation is 50% faster for large-scale data centers while maintaining similar quality of solution in terms of the energy consumption of data centers. • A progressive-fidelity approach developed for speeding up Genetic Algorithm. • 3 different fitness functions included for a quick and fine solution. • Heuristics are presented for the adaptive switching in our approach. [ABSTRACT FROM AUTHOR]

In recent years, water-side free cooling has been broadly applied in data centers to lessen the high cost of energy consumption. The energy-saving opportunities of free cooling have been recognized by numerous studies across the world. In this paper, the field test and energy performance evaluation were performed in an operational data center with water-side free cooling, located in Chongqing, China. Existing problems were found during tests, including the insufficient cooling capacity of towers in wintertime and the inability to activate partial-free cooling. The results of the tests show that the reduction in Power Usage Effectiveness (PUE) is much lower than the expected. To understand the marked discrepancies, the performance of the cooling tower was discussed in terms of the approach temperature and actual cooling capacity. The maximum cooling capacity in winter is only about 50 % of the rated. With the consideration of the cooling tower configuration performance in different climate conditions, the conservative energy effects were calculated in 34 provincial cities of China. To prolong the hours available, optimized operation schemes of cooling towers are presented. The available hours for free cooling could be increased from 730 h to 1300 h when operating at 67 % of rated flow rate, resulting in a decrease in the energy saving of chillers between 8.44 % and 21.05 %. [ABSTRACT FROM AUTHOR]

Due to the rapid rise of power consumption of data centers in recent years, much work has been done to develop energy-efficient design, controls and diagnosis of their cooling systems, while the energy system simulation is used as an effective tool. However, existing models of information technology (IT) equipment of data centers cannot well represent the effects of IT equipment design and operation status on the data center cooling demand, and this hinders the development of the energy saving cooling technologies of data centers. To address this issue, this paper introduces a power consumption model of IT equipment in data centers with coefficients and modeling script provided for immediate use in data center energy system simulation. This energy model can be used to simulate energy performance of typical IT equipment in data centers under real-time dynamic operation conditions conveniently and effectively without the need of data other than the specifications of a data center design and IT equipment manuals. Its use with a commonly used building simulation program is demonstrated with a building model of a typical large office in a subtropical area. The results show that the model can represent the change of power consumption of data centers with different IT equipment designs and operation appropriately. [ABSTRACT FROM AUTHOR]

Cloud computing has emerged as the most revolutionary technology in the field of computing. The cloud service providers (CSPs) have high computational facilities called data centers (DCs) at their disposal. CSPs provide services to the users through virtual machines (VMs). VM placement is the mapping of VMs onto physical machine called hosts. In this paper, we propose a two-tier virtual machine placement algorithm. Firstly, we propose a queueing structure to manage and schedule a large set of VMs. Secondly, a multi-objective VM placement algorithm called crow search based VM placement (CSAVMP) is proposed to reduce the resources wastage and power consumption at the data centers. VM migration is an indispensable part of any cloud platform for activities like maintenance, load balancing, fault tolerance etc. Three different migration strategies namely serial, parallel, improved serial have been tested and a comparative result has been produced. [ABSTRACT FROM AUTHOR]

The massive deployment of data center services and cloud computing comes with exorbitant energy costs and excessive carbon footprint. This demands green initiatives and energy-efficient strategies for greener data centers. Assignment of an application to different virtual machines has a significant impact on both energy consumption and resource utilization in virtual resource management of a data centre. However, energy efficiency and resource utilization are conflicting in general. Thus, it is imperative to develop a scalable application assignment strategy that maintains a trade-off between energy efficiency and resource utilization. To address this problem, this paper formulates application assignment to virtual machines as a profile-driven optimization problem under constraints. Then, a Repairing Genetic Algorithm (RGA) is presented to solve the large-scale optimization problem. It enhances penalty-based genetic algorithm by incorporating the Longest Cloudlet Fastest Processor (LCFP), from which an initial population is generated, and an infeasible-solution repairing procedure (ISRP). The application assignment with RGA is integrated into a three-layer energy management framework for data centres. Experiments are conducted to demonstrate the effectiveness of the presented approach, e.g., 23% less energy consumption and 43% more resource utilization in comparison with the steady-state Genetic Algorithm (GA) under investigated scenarios. [ABSTRACT FROM AUTHOR]

Virtual machine migration is an important method for achieving scalability through optimized resource utilization in the modern data centers. Most of the previous works concentrated on how to migrate the virtual machinewith huge traffic demands in the cloud environment. It mainly focused on energy consumed by the virtual machines and the network traffic between them. None of the earlier algorithms focused from the client’s perspective, like the delay faced by them to get their services processed. In this paper a new paradigm for virtual machine migration, based on the demands of the clients is considered. One of the clients demand is faster service time. To achieve this an algorithm called Virtual Machine Migration Approach Based on Distance and Traffic is developed. The methodology is based on the traffic and the geographical locations of the data centers. The algorithm is executed at regular intervals to check for the traffic in the network. It also checks for the distance of the data centers where the client request has to be placed. The request is placed to the nearest data center with less traffic. This improves the performance by reducing the round trip time. As the traffic is less so there is an optimized utilization of the resources. The reduction in round trip time and maintaining the round trip time without much fluctuation even in the case of a failure of one of the physical machinehelps to improve the performance by providing faster services to the clients. [ABSTRACT FROM AUTHOR]

Cloud computing, as a trending model for the information technology, provides unique features and opportunities including scalability, broad accessibility and dynamic provision of computing resources with limited capital investments. This paper presents the criteria, assets, and models for energy-aware cloud computing practices and envisions a market structure that addresses the impact of the quality and price of energy supply on the quality and cost of cloud computing services. Energy management practices for cloud providers at the macro and micro levels to improve the cost and reliability of cloud services are presented. [ABSTRACT FROM AUTHOR]

Air conditioning and cooling systems account for about 40 % of total electricity usage in data centers. Free cooling is a novel and promising technology that can decrease the load ratio of electrical chiller and save cooling energy consumption accordingly, through making full use of natural free cooling source. In this paper, four typical free cooling systems are analyzed and compared, to show their mechanisms, main features, energy saving effects and applicable situations respectively. (1) Direct fresh air cooling is of the highest free cooling potential. However, it is hard to meet indoor air quality demand due to the indoor-outdoor air mixing. (2) Rotating wheel heat exchanger can be used for indirect free cooling, since indoor and outdoor air flow in different paths for heat exchange. While its power usage effectiveness (PUE) increases inevitably under the same climatic conditions. (3) Heat pipe can be integrated with rack back plate to enhance heat transfer with free cooling sources. Its cooling efficiency can increase by 3-5 times compared to traditional heat exchangers. (4) In water-based free cooling system, a heat exchanger is installed in parallel with electrical chiller and the system can work under three modes according to different outdoor temperature. Increasing the load ratio of free cooling can decrease PUE and save electricity usage. In practical applications, the cooling system design for data centers depends on various factors, such as indoor air quality requirement, local climatic conditions, energy saving demands, room space, capital investment and operation costs. [ABSTRACT FROM AUTHOR]

The IT equipment power consumption estimated by building simulation software is much higher than the actual case, but there are no simple solutions for building engineers to reduce the bias in data center models in building simulation. This paper address this issue by proposing a simple model of IT equipment load. The model estimates the IT equipment load based on critical control inputs of the IT equipment such as its processor utilization rate and on/off status, and a survey of literature for the ordinary values of these IT equipment control variables is conducted to facilitate its uses by building engineers. A case study was conducted with building models of large offices with lots of IT equipment, and the estimated annual building energy use was reduced by more than 30% in all three cases. This shows that the conventional constant thermal load model overestimates the IT equipment load and the IT equipment model improves the accuracy of modeling of data centers in building simulation by modeling the changes of the load with the status of the servers. [ABSTRACT FROM AUTHOR]

The data centre air conditioning system has a huge energy consumption and has the characteristic of requiring year-round cooling. Cooling tower cooling technology can realize free cooling and reduce the operating time and energy consumption of the refrigeration host. In this paper, through the engineering examples, it discusses the cooling tower with free cooling of the air conditioning system combined with conventional refrigeration way design points. It analyses the way to realize the operation and transformation of conventional refrigeration mode, free cooling mode, partial free cooling mode and other operation modes through automatic control system. [ABSTRACT FROM AUTHOR]

A cooling plant by using lake water source was proposed to cool the space in data centers. It combines free cooling technology and variable capacity technology to remove heat and reduce energy consumption effectively. In this paper, firstly, the control strategies for this cooling plant were proposed. Then, a detailed annual energy consumption model with considering the effect of the dynamic heat dissipation characteristics of servers, lake water temperature, outdoor weather conditions, and cooling plant thermal performance, will be established to evaluate the performance and energy efficiency of this cooling plant under different load factors. The results show the mode 1 and mode 2 of this cooling plant are 67.6% and 32.4% of the year’s running time, respectively. The average EER of the cooling plant under different load factor ranges from 16.8~49.3, and the average PUE value ranges from 1.11 to 1.15 under different load factors, which demonstrates this cooling plant poses higher energy efficiency than the conventional cooling plant for the data centers. [ABSTRACT FROM AUTHOR]

In the more and more expanding and complicated data center, requirements on air conditioning system and the air distribution arrangement are getting higher and higher. One of the resolutions is improving the airflow uniformity and cooling the local over-hot area by raising the floor in the data center. With a typical mode of four rows of cabinets in the data center as a sample. This paper uses Computational fluid dynamic (CFD) to investigate the influence of the six raised floor heights (0.2m, 0.4m, 0.6m, 0.8m, 1m and 1.2m) on the airflow distribution of three types of under-floor air distribution (the open aisle, the cold aisle containment and the hot aisle containment). Optimization is done on performance indexes, including supply/return heat index (SHI/RHI), return cooling index (RCI) and return temperature index (RTI), with which the condition of subcooling / overheating of all cabinets and the characteristics of the airflow organization in the data center are reflected and the thermal environment in the data center is revealed. Normally, the cooling effect and the uniformity of the airflow can be improved by raising the floor height. However, this won’t work well when the floor height reaches a certain level. Therefore, based on the comparative analysis on the 18 different cases in this investigation, the recommended range of raised floor height in data centers of different construction can be obtained and verified in the experimental tests. The experimental results turn out to be in accordance with the simulation results, which proves the range recommended in this investigation is valuable. [ABSTRACT FROM AUTHOR]

• A job scheduling model for low-carbon data center operation is established. • Practical challenges such as job dependency and heterogeneity are considered. • A reinforcement learning algorithm is proposed to obtain online scheduling policy. • The effectiveness of proposed scheme is verified with real-world scenarios. As the digitalization of the economy and society accelerates, the enormous and fast-growing energy consumption of data centers is becoming a global concern. With the unique power consumption flexibility introduced by computing job scheduling, data centers could play an important role in enhancing the capability to integrate renewable generation as a demand-side resource. In this paper, we propose an online job scheduling scheme for low-carbon data center operation from an information and energy nexus perspective. We formulate the job scheduling problem as a Markov decision process in which job dependencies, job heterogeneity, and quality of service are considered comprehensively. To address the challenges of large-scale heterogeneous computing jobs, we propose a deep reinforcement learning-based approach to solve the energy-aware scheduling problem and achieve an optimal online policy. The case study results based on real-world data illustrate that the proposed scheme can effectively reduce the carbon footprint and energy cost of a data center while maintaining the quality of service for cloud products. [ABSTRACT FROM AUTHOR]

[Display omitted] • Discussed the current status of cooing technologies in data centers. • Introduced and reviewed existing phase change cooing in data centers. • Compared and summarized the characteristics of phase change cooling systems. • Analyzed the application limitations of phase change cooling technologies. • Explored the future research direction of phase change cooling technologies. With the advent of big data era, data centers (DCs) related energy use accounts for approximately 3% of the global electric power consumption. As the augmentation of data-processing performance and thermal density in DCs, its energy use will undoubtedly continuously burst. Meanwhile, the potential safety hazards, caused by IT equipment local overheating, threaten the safe operation and restrict the further development of DCs. Thus, efficient cooling approaches should be applied in DCs to ensure its safe operation and optimize its thermal environment and cooling efficiency. Phase change cooling (PCC) technology is regarded as one of the effective and widely-used cooling methods, which have been applied in DCs for several years. In this paper, the up-to-date PCC technologies are reviewed and summarized, as well as the latest progress in DC cooling field. Four main PCC technologies are discussed in this paper, namely independent heat pipe cooling, integrated heat pipe cooling, two-phase immersion cooling, and cold storage systems. Finally, the shortcomings of the current researches on the PCC methods are summarized, while some suggestions for future researches are provided to promote the application of PCC technologies and achieve safe operations and energy savings in data centers. [ABSTRACT FROM AUTHOR]

The use of indirect evaporative cooling technologies is an effective way to reach high energy efficiency systems and to reduce primary energy consumption. At present, interest in such systems is strongly increasing, with particular attention to data centers facilities. In fact, in these applications the indoor air temperature can be higher than the one of residential and commercial buildings, leading to a greater number of yearly operating hours of the system. In this paper an indirect evaporative cooler, based on a cross flow heat exchanger, has been tested and modelled. Many experiments have been carried out in typical data centers operating conditions, varying both water flow rate and inlet air conditions. A phenomenological model of the indirect evaporative cooler has been developed: the model takes into account the effects of the adiabatic cooling of the secondary air stream in the inlet plenum and the actual wettability of the heat exchanger surface. The model has been extensively validated and it is shown that simulation results are in very good agreement with experimental data. Therefore, it can be a suitable tool to design and to predict performance of indirect evaporative cooling systems. [ABSTRACT FROM AUTHOR]

Changing the whole network into a software defined network (SDN) is impractical due to high replacement cost. Therefore, there exist SDN hybrid networks, where SDN switches and legacy switches coexist at the same time. In this paper, we propose an SDN hybrid network architecture which can discover existence of legacy switches by using the Spanning Tree Protocol and thus have a global view of the SDN hybrid network. We also enable OpenFlow switches to cooperate with legacy switches by using the Learning Bridge Protocol without requiring any modification on legacy switches. By utilizing the characteristics of SDN, SDN applications can dynamically find routing paths according to pre-defined QoS requirements and current network status. We also propose a simulated annealing based QoS-aware routing (SAQR) algorithm which can adaptively adjust weights of delay, loss rate and bandwidth requirements in a cost function to find the best fit path according to QoS requirements. We evaluate the proposed SAQR in a simulated SDN hybrid network which runs applications with different QoS requirements. Simulation results show that the SAQR performs better than related work MINA in terms of the fitness ratios of delay, loss rate and bandwidth, with 88%, 90.8% and 86.5% of flows meeting their respective QoS requirements, in contrast to MINA, with only 63%, 82.4% and 87.5% of flows meeting their respective QoS requirements. [ABSTRACT FROM AUTHOR]

Although full-field simulations using computational fluid dynamics and heat transfer (CFD/HT) tools can be applied to predict the flow and temperature fields inside data centers, their running time remain the biggest challenge to most modelers. In this paper, a reduced order modeling method is used to drastically reduce the running time (up to 600 times faster) while still acceptably preserving the accuracy of the model. The results obtained from Proper Orthogonal Decomposition (POD) method are in good agreement with the results obtained from CFD/HT model simulation. The sensitivity analysis of some of the design parameters in the POD model is evaluated. In addition, a 3-D temperature profile of the data center model constructed from 2D slices are generated with a linear interpolation technique. Tradeoff between accuracy and running time is observed and discussed. [ABSTRACT FROM AUTHOR]

Cloud computing offers simplified system maintenance and scalable resource management with Virtual Machines (VMs). Users access resources of data centers by allocating VMs to hosts. Therefore, to improve the quality of cloud computing environment, not only the conventional multi Quality of Service (QoS) be satisfied, but also specific importance has to be made on certain metrics such as the system accessibility and resource scheduling in a cooperative and dynamic manner. This paper proposes a method called, Moving Average-based Fuzzy Resource Scheduling (MV-FRS) for virtualized cloud environment to optimize the scheduling of resources through virtual machines. Initially, the MV-FRS method starts by predicting the resource (i.e. bandwidth, memory and processing cycle) requirements. Then a measure of relationships between availability of resources and the requirements of resources are made. Finally, a fuzzy control theory is designed to accomplish system accessibility between user cloud requirements and cloud users resources availability. The simulations results demonstrate that the MV-FRS method is able to reduce the total waiting time of cloud user resource requirements and also ensure the feasibility and effectiveness of the overall system accessibility in terms of average success rate and resource usage when running in a cloud computing environment. [ABSTRACT FROM AUTHOR]