Data Centers In China Research Articles

Carbon emission scenario analysis of data centers in China under the carbon neutrality target

The low-carbon transformation of data centers is of great significance to achieve the goals of carbon peaking and carbon neutrality. This study compared and analyzed the overall situation of data centers in China. Based on China's CO2 emission and intensity targets in key years, the four variables of energy efficiency improvement rate, nonfossil energy consumption proportion, negative emission technology intensity, and waste energy utilization rate were introduced, and a net zero emission path model of data centers was established. Using scenario analysis to predict the total CO2 emissions and emission intensity from 2021 to 2060, three emission reduction path scenarios were obtained. Results showed that the energy consumption of data centers increased gradually, the carbon emissions first increased and then decreased, and the power usage effectiveness (PUE) of the data centers decreased gradually. The carbon peak time of the three scenarios is 2030, and the time for carbon neutrality is 2055, 2053, and 2051 in three scenarios. The data center industry should further improve the energy efficiency utilization rate, increase the proportion of nonfossil energy consumption, strengthen the technological innovation of carbon capture and storage, enhance the level of carbon sink, and optimize the utilization rate of waste energy.

Research on cooperative operation optimization method of internet data center alliance considering diversified flexibility

With the rapid development of large-scale internet data center in China, the goal of carbon peak and carbon neutrality has put forward higher requirements for low-carbon economic operation of internet data center. Considering complementary advantages among internet data centers, this paper proposes a cooperative operation optimization method of internet data center alliance based on diversified flexibility. Firstly, a cooperative operation structure of alliance considering electricity and carbon trading between members was built, as well as the diversified flexibility model considering carbon capture and power to gas device. Then, a cooperative optimization operation model of alliance was constructed based on Nash bargaining theory, and the allocation of cooperation benefit among members can be realized based on electrical carbon contribution rate. In order to improve solution convergence, the alternating direction multiplier method is improved by penalty factor updating and correction factor modeling method. Finally, the proposed method is applied to the demonstration project of “East Data and West Computing” in Qingyang, Gansu, China, and the trial run results are consistent with the theory analysis. On the one hand, the diversified flexibility and the electricity and carbon trading between members can effectively reduce the total cost and carbon emissions of alliance. On the other hand, the cooperation benefits distribution strategy based on the electrical carbon contribution rate can effectively enhance enthusiasm of members to participate in alliance. The improved alternating direction multiplier method can achieve distributed optimization of alliance, protect the privacy of each member, and has good convergence and scalability.

A Newly Established Air Pollution Data Center in China

A Newly Established Air Pollution Data Center in China

英国科学数据发展政策与规划

The United Kingdom (UK) stands as a pioneer in offering a clear definition of scientific data/research data, setting a global standard with its policies on scientific data. The UK’s data development strategy, planning, and action plans have effectively positioned it as a global leader in the realm of scientific data management, sharing, and reuse. This paper conducts a systematic review of the UK's scientific data policies, supporting institutions, and development plans through a comprehensive analysis of literature, websites, and other information resources. The results illustrate that the UK has established a comprehensive policy framework for scientific data management across various levels, encompassing government departments, supporting institutions, and research organizations. This framework revolves around the development, management, sharing, and application of scientific data, and incorporates strategic planning and action plans at both the national and regulatory levels. These initiatives have laid a solid foundation for the UK’s international leadership in the open sharing and utilization of scientific data. Notably, the UK’s practice of incorporating scientific data centers into national infrastructure construction ensures the continuous accumulation, management, sharing, and efficient utilization of scientific data assets within the country. The UK’s policy system for scientific data development and its planning practices in fostering such progress can serve as a valuable reference for the establishment of scientific data centers in China.

Process-based evaluation of carbon emissions from the on-site construction of prefabricated steel structures: A case study of a multistory data center in China

Prefabricated steel structures are being promoted in China as an important measure to reduce building carbon emissions (CEs). Currently, high-quality carbon emission factors and calculation methods that identify the sources of CEs from construction process are lacking. This study proposed a process-based method for calculating on-site construction CEs and empirically demonstrated its feasibility using a case of a multistory steel structure with typical column-beam systems. Six construction processes comprising 38 subprocesses were first categorized, then the energy and materials consumed for each subprocess were determined to obtain the first-hand CE parameters. The CEs from hoisting the first- and second-section columns were 10.52 kgCO2/column and 14.41 kgCO2/column, respectively, and the CEs from their installation were in the range 38.16–69.18 kgCO2/column. The CEs from hoisting the primary and secondary beams using a car crane were 7.73 kgCO2/beam and 4.52 kgCO2/beam, respectively, with lower CEs of 2.40 kgCO2/beam and 2.22 kgCO2/beam, respectively when a tower crane was used for hoisting. The 14 connection types used to install the primary beams and the 11 connection types used to install the secondary beams had CEs of 13.84–125.96 kgCO2/beam and 10.39–43.49 kgCO2/beam, respectively. The total on-site construction CE was subsequently calculated by summing the CEs from each process. This method was able to track the CE sources for each construction process and their contributions to identify those with high CE intensities. In addition, the case study was evaluated to provide specific feedback on the current national standard for calculating CEs from building construction.

Climatic applicability of indirect evaporative cooling strategies for data centers in China

Modern digital developments have propelled data center (DC) to prominence, with the cooling requirement being an important driver of its energy consumption. This study explores the climate application potential of indirect evaporative cooling (IEC) technology, which utilizes water evaporation for cooling. The IEC technology is considered as a promising avenue for energy-efficient DC cooling system. This study reveals essential insights for optimizing DC cooling performance by analyzing different IEC operation modes, including dry mode, wet mode, and cascading hybrid mode. The selection of operation mode is closely related to climatic conditions, DC working conditions, and IEC cooling efficiency. Higher DC supply air temperatures extend dry mode cooling hours and decrease cascading hybrid mode hours. It enhances energy savings through better utilization of natural free cooling resources. Elevating IEC cooling efficiency expands dry mode cooling hours while reducing reliance on mechanical cooling, contributing to improved power usage effectiveness (PUE) and energy conservation. The analysis is based on a typical data center and considers representative cities in different climate zones of China, emphasizing the importance of reducing the PUE and energy consumption of the DC by adjusting the DC supply air temperature and improving the IEC cooling efficiency. This work provides a useful reference for customizing DC cooling strategies according to various climate differences, optimizing energy efficiency and promoting sustainable development.

Cooling Technologies for Internet Data Center in China: Principle, Energy Efficiency, and Applications

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.

Facilitating the provision of load flexibility to the power system by data centers: A hybrid research method applied to China

With the increasing penetration of renewables, load flexibility is crucial to balance the power system in China. Data centers are rapidly growing energy consumers and their flexible operations could make them a potential source of load flexibility. However, there have been no large-scale attempts to apply this concept in China. This paper utilizes a hybrid research method to explore the flexibility potentials, identifying key technical, financial, policy, and institutional barriers. The study quantifies the flexibility potential of data centers in China and provides solutions for addressing the identified solutions.

A comprehensive analysis of green building rating systems for data centers

The high energy consumption of data centers (DCs) has become a global issue that needs to be addressed, and as a result, their development of green rating systems (GBRSs) has been widely emphasized. Although GBRSs have been the subject of many studies for various building types, there has been few studies for DCs. This is the first study to comprehensively analyze GBRSs for DCs. The study established nine categories of standardized overall indicators and seven categories of standardized energy indicators, and furthermore reformatted and aligned the GBRS items accordingly. The study first compared GBRSs for DCs and other non-residential buildings to find out main concerns for a green DC, second compared different GBRSs for DCs to identify regional differences, and finally investigated GBRSs for DCs in China where digital infrastructure is booming. Power usage effectiveness (PUE) as a key indicator of DC energy efficiency was further analyzed. The results highlighted the importance of energy related criteria to DCs and discussed how to use the assessment criteria to appropriately guide and promote green DCs in future work. It also discussed the constraint for energy efficiency in DCs and the best practices that should be encouraged in the category of Innovation for green DCs. This study fills the gap in existing studies of GBRSs for DCs and contributes to the development of green buildings and the refinement of assessment criteria in DCs.

Utilizing landfill gas (LFG) to electrify digital data centers in China for accelerating energy transition in Industry 4.0 era

The volume of landfill gas (LFG) produced during municipal solid waste (MSW) decomposition in local landfills has increased rapidly in recent years. To promote circular economy (CE) applications through resource recovery, this article investigates the feasibility of utilizing LFG as a sustainable energy to power data centers (DCs) in Xiamen (China) and evaluates the implications of waste heat reuse on the energy consumption of DC. The objective of this study was to investigate the environmental impacts and economic benefits of this scheme based on a SFC model. With respect to its novelty, LFG is utilized as an energy supply to DCs, while the waste heat generated in the energy production is recovered. The socio-economic benefits generated in this process are also elaborated. It was found that a large number of MSW was completely recycled to generate energy. The LFG enterprise could benefit from energy sale, while the energy satisfied the annual demand for DC power consumption. Utilizing this energy output resulted in the reduction of primary energy and less CO₂ emissions. By reusing the waste heat for domestic heating, the energy efficiency could be improved, while primary energy could be further saved, avoiding more CO₂ emissions. This implies that utilizing LFG output and waste heat to power DCs not only facilitate resource recovery from unused resources to improve energy efficiency and obtain socio-economic benefits, but also promote the digitalization of DCs and sustainable development of urban cities in the framework of CE.

Energy-efficient SDN for Internet of Things in smart city

The establishment of smart city database requires Internet of things technology. With the continuous expansion of the scale of the Internet of things data center, the energy consumption of the data center is increasing, which greatly limits the development of the Internet of things data center. Energy consumption of Internet of things network data center in China's smart city has increased year by year, and data center network energy conservation has become a current research hotspot. The traditional network nodes are distributed and centralized innovatively to complete centralized control, and the controller completes the collection of the whole network information, the maintenance of network status, the distribution of flow entry, etc. Firstly, the energy-saving model of digital center network and the model data center network traffic prediction algorithm based on the principle of high accuracy traffic prediction are constructed, and an energy-saving multi-layer virtual traffic scheduling algorithm is proposed. Secondly, the two algorithms are fused, and finally an empirical study is carried out. The results show that in Random mode, the energy consumption ratio of energy-efficient multi-layer virtual-software defined networking (EMV-SDN) is the lowest, and the maximum reduction of energy consumption ratio reaches 7.8% compared with equal-cost multi-path (ECMP) algorithm. In Staggered mode and Stride mode, the energy consumption ratio of EMV-SDN algorithm is the lowest. Compared with the actual data flow, the prediction result of K-means-support vector machine (KM-SVM) algorithm is closer to the actual result, and the maximum error between the predicted value and the actual value of KM-SVM algorithm is 1.2 ​Gbps. However, the maximum error of balanced iterative reducing and clustering using hierarchies-support vector machine (B-SVM) algorithm reaches 3.1 ​Gbps; the prediction accuracy of KM-SVM algorithm is always higher than that of B-SVM algorithm in both discontinuous data flow and continuous actual data flow; the accuracy of KM-SVM algorithm in different experiments is high; among the energy consumption ratios of the combined algorithm, the energy consumption ratio of EMV-SDN algorithm is the lowest under the three communication modes, and the performance of the algorithm constructed in this study is higher than that of other algorithms in simulation operation; when the traffic prediction algorithm is combined with the virtual topology energy conservation control algorithm, the network structure change is reduced and the network stability is increased in this study. In the delay comparison of EMV-SDN algorithm, ECMP algorithm, and Dijkstra algorithm in the three communication modes, the EMV-SDN algorithm has the best performance. The results of this study provide an improvement direction for the development of Internet of Things technology and the construction of smart cities.

Energy efficiency and economic analysis of utilizing magnetic bearing chillers for the cooling of data centers

Recently, with the rapid construction of data centers in China, the server installed ratios of new data centers are generally small in the early stage, which makes the cooling systems usually operate under the part cooling load conditions. Therefore, it is significant to select chillers that can operate efficiently with part load ratios. However, while the magnetic bearing chillers have been proven high efficiency chillers under part load conditions in the previous studies, the application of magnetic bearing chillers in the data centers has not been investigated clearly. In this paper, the energy performance and economy of utilizing the magnetic bearing chiller for the cooling of the data center are simulated and compared with that of the traditional centrifugal chiller and scroll chiller comprehensively. The simulation model is validated by the field test results. Furthermore, the energy efficiency of the data center cooled by the magnetic bearing chiller is discussed according to the long-term monitoring data of a small-scale data center of a university in Nanjing China. The results show that the average COP of the magnetic bearing chiller varies from 8 to 10 under different load conditions, which is much higher than that of the traditional centrifugal chiller and the scroll chiller especially when the server installed ratio of the data center is lower than 50%. The energy saving rate of the magnetic bearing chiller are approximately 10%∼40% compared with the traditional centrifugal chiller and the scroll chiller. In terms of economy, the initial investment of the magnetic bearing chiller is much higher than that of the traditional centrifugal chiller and scroll chiller while the operation cost of the magnetic bearing chiller is the lowest. This study benefits the selection of chillers for this kind of small-scale data centers.

Increasing the energy efficiency of a data center based on machine learning

AbstractEnergy efficiency of data centers (DCs) is of great concern due to their large amount of energy consumption and the foreseeable growth in the demand of digital services in the future. The past decade witnessed improvements of the energy efficiency of DCs from an extensive margin—a shift from small to large, more efficient DCs. Improvements from the intensive margin, that is, from more efficient operation, would be critical in limiting the energy consumption and environmental impact of DCs in the upcoming period. Machine learning algorithms have advantages in optimizing the operation of DCs to improve energy efficiency as they have shown the potential of discovering control strategies not found by traditional method, and producing working condition‐dependent control strategies. This study proposes ready‐to‐use machine learning methods with practical details to decrease the most commonly used energy efficiency metric—power usage effectiveness in DCs. We achieved an accurate prediction by properly selecting the features used in the proposed prediction models established by neural network, light gradient boosting machine, recurrent neural network, and random forests. The proposed approaches are implemented in one of the largest hyperscale DCs in China—Tencent Tianjin DC, to optimize the set points of controllable variables in the cooling system and to detect and adjust the unreasonable working conditions in the modular data centers. The lower bound of PUE reduction was 0.005 with the proposed approaches, leading to about 1500 MWh (0.24% of the total designed electricity consumption of this DC) of energy saved per year in this hyperscale DC. The proposed methods have the potential to be transferred to DCs of similar scale, and the framework of our work could serve as a guide for machine learning‐based optimization of environmental indicators in other complex product/service systems.

Optimal design of heat dissipation based on the first seabed data center in China

In recent years, with the rapid development of big data and cloud computing technology, large Internet companies have taken the ocean with natural heat dissipation advantages as the location of data centers. However, there are still many complex factors to consider in the construction of data centers in seawater. This paper is based on the background of the first submarine data module in China.

Thermodynamic optimization of a rack-level water-cooling infrastructure in high load data centers based on the principle of minimum exergy destruction

Exergy model and the least exergy loss principle are introduced to analyze the exergy loss distribution in heat transfer and air/water flow process, for a combined server/cabinet cooling network. Based on the optimized case suggested by the exergy model, a multi-stage water cooling scheme is designed, with two-stage heat exchanger in server cabinet, and double-chiller in the water loop. Both thermal and energy performance of such cooling infrastructure has been tested in a data center in China, with the measured performance in good agreement with the designed values, and the exergy loss model has been validated.

Data Correlation based Feature Selection Model for Children’s Growth and Development Assessment

The paper focuses on children’s data present inElectronic Health Records(EHRs) starting fromraw data gathered from Southwest Medical Data Center in China. We designed a systembased on workflow to analyzefeatures that affect children’s growth. The system is able to relate health caredata to diagnosis codes and with additional information integrated and correlated to EHRs data. Finally, we propose prediction models based on Recursive Feature Elimination (RFE), which can identify features thatare important to detect children’s growthand findcorrelations among features.

Optimization of the thermal environment of a small-scale data center in China

In data centers, a large number of IT sever racks with different heat dissipation rates are arranged in multiple rows, leading to much higher per-unit heat release and therefore more uneven temperature distribution than in general buildings. This paper chooses a small-scale data center located in China to analyze the thermal environment for the operation of these critical equipments. The wind speed and temperature in the data center were measured, and a corresponding computer room model was established via computational fluid dynamics (CFD) simulation software. Based on it, the characteristics of flow field and temperature field of the data center were analyzed. The optimization methods for problem of uneven temperature distribution and chaotic air supply in the data center were proposed and verified by evaluation index. Furthermore, the return temperature index(RHI) increased from 0.918 to 0.93, the return heat index(RTI) increased from 0.222 to 0.342.

Cloud Control: China’s 2017 Cybersecurity Law and its Role in US Data Standardization

This paper contends that by influencing the security issues presented by global corporate data investments through a combination of domestic policymaking, dominance in key market sectors, and leadership in international standards-making organizations, China is establishing new global standards for the data trade. In 2017, Apple Inc. was one of the first firms to build a new joint venture data center in China to comply with China’s Cybersecurity Law of the same year. This law requires all firms that maintain “critical information infrastructure” to store their data on a Chinese-owned server. Apple, like many firms operating in China, relies heavily on data centers to function in the Chinese market. However, for Apple, as for many other companies in areas ranging from engineering services to enterprise computing, the decision to open a data center with major ownership by a Chinese firm transforms the politics of power and access to data within the company and among its consumers. Indeed, shortly after Apple began migrating the data of Chinese users to its new Chinese servers, some US-based users reported the migration of their data to Chinese servers as well. The effects of Chinese influence on data security standards in the United States have yet to be thoroughly explored in the communications scholarship. No one has done so in relation to data storage for firms that are not explicitly in the digital economy such as white goods providers and home monitoring systems,. Drawing on reviews of Chinese corporate filings and government documents, as well as U.S. corporate filings this paper concludes that China’s Cybersecurity Law and the related technology regulatory framework have fundamentally transformed the ownership and the circulation of the data within the US market. The findings of this paper suggest that the United States should change its approach, because its current laissez-faire policies ultimately protect neither consumers nor national competitiveness. China’s influence on global data governance lies at the core of the future of global communications policy. Targeted, critical perspectives on the country’s specific policies and their effects on firms across a wide range of sectors will help regulators to plan for mid- and long-term strategic challenges not just in the technology industry, but across sectors. As more industrial sectors become data-driven, communications policy will take an increasingly central role in industrial regulation writ large.

Summary of the 2 nd China Power Industry Data Center Summit Forum

[Introduction] With the development of cloud computing, big data, artificial intelligence, 5G, and ubiquitous Internet of Things, the data center for the power industry is to improve the planning and leading of the data center of the power industry and the full life cycle digital service capability of the system integration. Scholars and builders provide an academic exchange platform. The 2nd China Power Industry Data Center Summit Forum was held in Guangzhou on May 9, 2019. [Method] The conference was co-sponsored by the Data Center Working Group of the Information Engineering Professional Committee of the China Engineering Construction Standardization Association and the China Energy Construction Group Guangdong Electric Power Design and Research Institute Co., Ltd., and hosted by the China Institute of Intelligent Information. [Result] This forum will focus on the construction of China′s power industry data center, technology leapfrog development, industry innovation and integration; in the environment of the fourth industrial revolution led by digital, network and intelligent, co-operate with electricity Cross-border development and technology integration with data centers. [Conclusion] The conference is of great significance to promote the development of data centers in China′s power industry.

Prospects for national biological big data centers

In the era of big data, scientific big data have become the new driving force for both science and technology innovation and social and economic development. China is a powerhouse in generating vast quantities of biological data, which are an essential strategic resource for population health and national security. The current situation of data loss due to the isolated data storage and the lack of systematic data monitoring and management, and the heavy dependency on international biological data centers urgently calls for China's own life big data storage and management system at the national level. Taking NCBI as an example, this article introduces the development history and present situation of the international biological big data centers. In addition, the importance, urgency, current historical opportunity and prospect of establishing a national biological big data center in China are also expounded in detail. In order to promote the development of the national center and improve China's international competitiveness and influence in life science research, the BIG Data Center at Beijing Institute of Genomics (BIG), Chinese Academy of Sciences, has taken many efforts on big data deposition, integration and translation and achieved initial progress.