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Journal articles on the topic 'Data centers management'

Author: Grafiati

Published: 8 March 2025

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1

El-Sayed, Nosayba, Ioan A. Stefanovici, George Amvrosiadis, Andy A. Hwang, and Bianca Schroeder. "Temperature management in data centers." ACM SIGMETRICS Performance Evaluation Review 40, no. 1 (June 7, 2012): 163–74. http://dx.doi.org/10.1145/2318857.2254778.

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2

ZHANG, Wei, Ying SONG, Li RUAN, Ming-Fa ZHU, and Li-Min XIAO. "Resource Management in Internet-Oriented Data Centers." Journal of Software 23, no. 2 (March 6, 2012): 179–99. http://dx.doi.org/10.3724/sp.j.1001.2012.04146.

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3

Mukherjee, Tridib, Ayan Banerjee, Georgios Varsamopoulos, and Sandeep K. S. Gupta. "Model-driven coordinated management of data centers." Computer Networks 54, no. 16 (November 2010): 2869–86. http://dx.doi.org/10.1016/j.comnet.2010.08.011.

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4

TAKANO, Ryousei, and Kuniyasu SUZAKI. "Disaggregated Accelerator Management System for Cloud Data Centers." IEICE Transactions on Information and Systems E104.D, no. 3 (March 1, 2021): 465–68. http://dx.doi.org/10.1587/transinf.2020edl8040.

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5

Assi, Chadi, Sara Ayoubi, Samir Sebbah, and Khaled Shaban. "Towards Scalable Traffic Management in Cloud Data Centers." IEEE Transactions on Communications 62, no. 3 (March 2014): 1033–45. http://dx.doi.org/10.1109/tcomm.2014.012614.130747.

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Kumar, Sanjay, Vanish Talwar, Vibhore Kumar, Parthasarathy Ranganathan, and Karsten Schwan. "Loosely coupled coordinated management in virtualized data centers." Cluster Computing 14, no. 3 (March 7, 2010): 259–74. http://dx.doi.org/10.1007/s10586-010-0124-9.

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7

Chu, Wen-Xiao, and Chi-Chuan Wang. "A review on airflow management in data centers." Applied Energy 240 (April 2019): 84–119. http://dx.doi.org/10.1016/j.apenergy.2019.02.041.

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8

Wu, H., A. N. Tantawi, Y. Diao, and W. Wang. "Adaptive memory load management in cloud data centers." IBM Journal of Research and Development 55, no. 6 (November 2011): 5:1–5:10. http://dx.doi.org/10.1147/jrd.2011.2170869.

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9

Mishra, Mayank, and Umesh Bellur. "Unified resource management in cloud based data centers." CSI Transactions on ICT 5, no. 4 (April 17, 2017): 361–74. http://dx.doi.org/10.1007/s40012-017-0168-6.

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10

Sunday Adeola Oladosu, Adebimpe Bolatito Ige, Christian Chukwuemeka Ike, Peter Adeyemo Adepoju, Olukunle Oladipupo Amoo, and Adeoye Idowu Afolabi. "Revolutionizing data center security: Conceptualizing a unified security framework for hybrid and multi-cloud data centers." Open Access Research Journal of Science and Technology 5, no. 2 (August 30, 2022): 086–76. https://doi.org/10.53022/oarjst.2022.5.2.0065.

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The rapid shift towards hybrid and multi-cloud environments has introduced significant security challenges for data centers, as traditional security models struggle to meet the demands of modern infrastructures. This review conceptualizes a unified security framework aimed at revolutionizing data center security in the context of hybrid and multi-cloud architectures. The proposed framework integrates on-premise and cloud security controls into a cohesive, scalable solution that addresses the complexities of modern data centers, ensuring robust protection against increasingly sophisticated cyber threats. At the core of the framework is a centralized security management platform that enables real-time monitoring, policy enforcement, and incident response across diverse environments. The integration of Zero Trust Architecture ensures that security is applied rigorously, with continuous authentication and authorization for all access requests, irrespective of the user's location. Additionally, the framework leverages artificial intelligence (AI) and machine learning (ML) to enhance threat detection and response capabilities. AI-driven analytics enable the identification of anomalous activities, vulnerability scanning, and predictive threat intelligence, offering faster and more accurate responses to emerging security threats. The framework also emphasizes data protection through advanced encryption methods, securing sensitive information both in transit and at rest across hybrid and multi-cloud environments. Automated compliance management tools ensure that data centers remain compliant with industry standards and regulations, such as GDPR and CCPA, through continuous monitoring and real-time auditing. By incorporating automation, the framework reduces operational complexity, minimizing human error and ensuring consistency in policy enforcement across various platforms. This unified security framework promises to enhance the security posture of hybrid and multi-cloud data centers, reduce operational overhead, and improve compliance management, ultimately providing organizations with a scalable, adaptable, and proactive solution for safeguarding their digital infrastructure in an increasingly complex cyber landscape.

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11

Miller, Holmes E., and Kurt J. Engemann. "Business Continuity Management in Data Center Environments." International Journal of Information Technologies and Systems Approach 12, no. 1 (January 2019): 52–72. http://dx.doi.org/10.4018/ijitsa.2019010104.

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In this article, the authors discuss how business continuity methodologies can be used by data centers to respond to natural disasters, man-made disasters, and accidents. Because organizations depend on computing services, which may become unavailable when disasters strike, prudent risk management processes can provide for continuation and recovery of operations. With a focus on data centers, this article discusses the business continuity plan development process. This article also considers elements of a business continuity management plan, which includes strategy development, preparedness, mitigation, exercises, and response and recovery, and discuss business continuity strategies for colocation and cloud-based data center architectures. Finally, the authors discuss how the ordered weighted average (OWA) methodology can be used to incorporate a decision makers risk profile when confronted with decisions related to the processes discussed.

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12

Patnaik, Debprakash, Manish Marwah, Ratnesh K. Sharma, and Naren Ramakrishnan. "Temporal data mining approaches for sustainable chiller management in data centers." ACM Transactions on Intelligent Systems and Technology 2, no. 4 (July 2011): 1–29. http://dx.doi.org/10.1145/1989734.1989738.

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13

Guo, Le Jiang, Feng Zheng, Ya Hui Hu, Lei Xiao, and Liang Liu. "Analysis and Research of Cloud Computing Data Center." Applied Mechanics and Materials 427-429 (September 2013): 2184–87. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.2184.

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Cloud computing data centers can be called cloud computing centers. It has put forward newer and higher demands for data centers with the development of cloud computing technologies. This paper will discuss what are cloud computing data centers, cloud computing data center construction, cloud computing data center architecture, cloud computing data center management and maintenance, and the relationship between cloud computing data centers and clouds.

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14

Rajput, Ravindra Kumar Singh, Dinesh Goyal, Anjali Pant, Gajanand Sharma, Varsha Arya, and Marjan Kuchaki Rafsanjani. "Cloud Data Centre Energy Utilization Estimation." International Journal of Cloud Applications and Computing 12, no. 1 (January 1, 2022): 1–16. http://dx.doi.org/10.4018/ijcac.311035.

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Due to the growth of the internet and internet-based software applications, cloud data center demand has increased. Cloud data centers have thousands of servers that are 24×7 working for users; it is the strong witness of enormous energy consumption for the operation of the cloud data center. However, server utilization is not remaining the same all the time, so, from an economic feasibility point of view, energy management is an essential activity for cloud resource management. Some well-known energy management techniques for cloud data centers generally used are dynamic voltage and frequency scaling (DVFS), dynamic power management (DPM), and task scheduling-based techniques. The present work is based on an analytical approach to integrating resource provisioning with sophisticated task scheduling; the authors estimate energy utilization by cloud data centers using iDR cloud simulator. The work is intended to optimize power consumption in the cloud data center.

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15

Bhardwaj, Rakhi, R. Padmavathy, M. Preetha, R. Suresh, Yogendra Kumar, S. Dilip, and S. Tharmar. "EMS for Sustainable Data Centers." E3S Web of Conferences 591 (2024): 01006. http://dx.doi.org/10.1051/e3sconf/202459101006.

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With the rapid expansion of cloud computing, data centers have become one of the largest consumers of energy globally. To achieve energy efficiency and sustainability, integrating renewable energy sources such as solar and wind into the energy management system (EMS) of data centers has become essential. This paper proposes an intelligent EMS framework designed for sustainable data centers, which dynamically balances energy loads between renewable energy generation, battery storage, and grid supply. The system optimizes energy consumption by leveraging real-time data from renewable sources, minimizing grid dependency, and reducing overall operational costs. A simulation over a 24-hour period demonstrates that the EMS can significantly reduce grid power usage while maintaining data center load demands, thereby supporting greener and more efficient data center operations.

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16

Jason, Sebagenzi, and Suchithra R. "Self-Organizing Architecture In Data Centers For Power Management." International Journal of Computer Sciences and Engineering 6, no. 9 (September 30, 2018): 97–104. http://dx.doi.org/10.26438/ijcse/v6i9.97104.

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17

Islam, Raihan Ul, Xhesika Ruci, Mohammad Shahadat Hossain, Karl Andersson, and Ah-Lian Kor. "Capacity Management of Hyperscale Data Centers Using Predictive Modelling." Energies 12, no. 18 (September 6, 2019): 3438. http://dx.doi.org/10.3390/en12183438.

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Big Data applications have become increasingly popular with the emergence of cloud computing and the explosion of artificial intelligence. The increasing adoption of data-intensive machines and services is driving the need for more power to keep the data centers of the world running. It has become crucial for large IT companies to monitor the energy efficiency of their data-center facilities and to take actions on the optimization of these heavy electricity consumers. This paper proposes a Belief Rule-Based Expert System (BRBES)-based predictive model to predict the Power Usage Effectiveness (PUE) of a data center. The uniqueness of this model consists of the integration of a novel learning mechanism consisting of parameter and structure optimization by using BRBES-based adaptive Differential Evolution (BRBaDE), significantly improving the accuracy of PUE prediction. This model has been evaluated by using real-world data collected from a Facebook data center located in Luleå, Sweden. In addition, to prove the robustness of the predictive model, it has been compared with other machine learning techniques, such as an Artificial Neural Network (ANN) and an Adaptive Neuro Fuzzy Inference System (ANFIS), where it showed a better result. Further, due to the flexibility of the BRBES-based predictive model, it can be used to capture the nonlinear dependencies of many variables of a data center, allowing the prediction of PUE with much accuracy. Consequently, this plays an important role to make data centers more energy-efficient.

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18

Han, Zhenhua, Haisheng Tan, Rui Wang, Guihai Chen, Yupeng Li, and Francis Chi Moon Lau. "Energy-Efficient Dynamic Virtual Machine Management in Data Centers." IEEE/ACM Transactions on Networking 27, no. 1 (February 2019): 344–60. http://dx.doi.org/10.1109/tnet.2019.2891787.

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19

T, Veni, and Mary Saira Bhanu S. "Dynamic Energy Management In Cloud Data Centers: A Survey." International Journal on Cloud Computing: Services and Architecture 3, no. 4 (August 31, 2013): 13–26. http://dx.doi.org/10.5121/ijccsa.2013.3402.

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20

Cziva, Richard, Simon Jouet, David Stapleton, Fung Po Tso, and Dimitrios P. Pezaros. "SDN-Based Virtual Machine Management for Cloud Data Centers." IEEE Transactions on Network and Service Management 13, no. 2 (June 2016): 212–25. http://dx.doi.org/10.1109/tnsm.2016.2528220.

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21

Li, Jianxiang, and Youchun Zhang. "Request–Response Distributed Power Management in Cloud Data Centers." Journal of Intelligent Systems 22, no. 4 (December 1, 2013): 437–51. http://dx.doi.org/10.1515/jisys-2013-0015.

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AbstractPower provision is coming to be the most important constraint to data center development. The efficient management of power consumption according to the loads of the data center is urgent. As the load for every application hosted in every server node (SN) of the data center and corresponding Service Level Agreement (SLA) requirements can be quite different, it is hard to deploy a power strategy at application. The asynchronies and abruptness characteristics of workload fluctuation make power management policymaking using periodic resource scheduling method invalid. In this article, the design and implementation of the request–response distributed power management scheme is elaborated. Bound by linear time complexity, the method proposed integrates dynamic voltage/frequency scaling, power-on–power-off, and virtual machine migration mechanisms and dynamically optimizes the power consumption of a cloud data center. The significant advantage of the scheme is that it does not need synchronous scheduling between all SNs. Simulation results showed that the scheme could effectively decrease the power consumption of the data center, with a tiny reduction in performance as centralized control methods.

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22

Garimella, Suresh V., Lian-Tuu Yeh, and Tim Persoons. "Thermal Management Challenges in Telecommunication Systems and Data Centers." IEEE Transactions on Components, Packaging and Manufacturing Technology 2, no. 8 (August 2012): 1307–16. http://dx.doi.org/10.1109/tcpmt.2012.2185797.

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23

Li, Jie, Zuyi Li, Kui Ren, and Xue Liu. "Towards Optimal Electric Demand Management for Internet Data Centers." IEEE Transactions on Smart Grid 3, no. 1 (March 2012): 183–92. http://dx.doi.org/10.1109/tsg.2011.2165567.

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24

Wang, Huangxin, Jean X. Zhang, Bo Yang, and Fei Li. "On time-sensitive revenue management in green data centers." Sustainable Computing: Informatics and Systems 14 (June 2017): 1–12. http://dx.doi.org/10.1016/j.suscom.2017.01.002.

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25

Chen, Hui, Ping Lu, Pengcheng Xiong, Cheng-Zhong Xu, and Zhiping Wang. "Energy-aware application performance management in virtualized data centers." Frontiers of Computer Science 6, no. 4 (August 2012): 373–87. http://dx.doi.org/10.1007/s11704-012-2107-x.

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26

Chen, Tianyi, Yu Zhang, Xin Wang, and Georgios B. Giannakis. "Robust Workload and Energy Management for Sustainable Data Centers." IEEE Journal on Selected Areas in Communications 34, no. 3 (March 2016): 651–64. http://dx.doi.org/10.1109/jsac.2016.2525618.

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27

Dasgupta, Gargi, Amit Sharma, Akshat Verma, Anindya Neogi, and Ravi Kothari. "Workload management for power efficiency in virtualized data centers." Communications of the ACM 54, no. 7 (July 2011): 131–41. http://dx.doi.org/10.1145/1965724.1965752.

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28

Amin, Atif, Raul Valverde, and Malleswara Talla. "Risk Management via Digital Dashboards in Statistics Data Centers." International Journal of Information Technologies and Systems Approach 13, no. 1 (January 2020): 27–45. http://dx.doi.org/10.4018/ijitsa.2020010102.

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Every system, when connected to a network, is susceptible to threat of being hacked. It is important to protect all systems of an organization in real-time in a cost-effective manner. This article presents a well-designed and integrated database for risk management data using a dashboard interface in real-time risk that makes it easy for risk managers to reach a understanding the level of threats to be able to apply right controls to mitigate them. In this article, a case study of a data center for a statistical management institute is presented that proposes the calculation of total risk at the organization level by using the proposed risk database. A digital dashboard is also designed for presenting the risk level results so that decision makers can apply counter measures. The risk level on a dashboard viewer makes it easy for decision maker to understand the overall risk level at the statistics data center and assists in the creation of a tool to follow-up risk management since the time a threat hits until the time of its mitigation.

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29

Guitart, Jordi. "Toward sustainable data centers: a comprehensive energy management strategy." Computing 99, no. 6 (June 28, 2016): 597–615. http://dx.doi.org/10.1007/s00607-016-0501-1.

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30

Dzyuba, Anatolyy, Irina Solovyeva, and Dmitry Konopelko. "Managing Electricity Costs in Industrial Mining and Cryptocurrency Data Centers." International Journal of Energy Economics and Policy 13, no. 4 (July 9, 2023): 76–90. http://dx.doi.org/10.32479/ijeep.13955.

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2022 has seen a significant decline in global cryptocurrency ratings, especially bitcoin. As it is known, one of the key components of the cryptocurrency’s cost is the amount of electrical energy spent by the computing equipment of the mining data centers. In the context of declining bitcoin rates, the management of the data centers’ energy costs becomes critical for maintaining the profitability and investment return of the mining projects. Russia is among the top-3 leading producers of cryptocurrencies, providing 11% of the global primary bitcoin transactions. In this regard, the management of the data centers’ costs related to the purchase of electricity in the Russian wholesale and retail electricity (capacity) markets presents a high scientific and practical importance. This article analyzes the pricing mechanisms for the purchase of electricity in Russia’s wholesale and retail electricity (capacity) markets on an industrial scale given the specifics of the hourly demand-based pricing. This paper suggests a new metrics system, including a capacity demand management coefficient and a transmission cost management coefficient, which allow setting specific price parameters for the different components of the electricity price based on demand analytics. Simulation of different parameters of the energy cost management in mining data centers demonstrated that the ultimate electricity price can, on average, be reduced by 70% of the initial level across all regions of the Siberian Federal District of Russia. The suggested energy cost management model takes into account both internal and external factors of industrial data centers as well as monitoring of their operations along with the price factors of the wholesale and retail electricity markets. This material may be useful to specialists in the field of management of mining data centers who are involved in the operation and/or design of such facilities across various regions of Russia.

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Subbiah, Sankari, Perumal Varalakshmi, R. Prarthana, and C. Renuka Devi. "Energy Efficient Big Data Infrastructure Management in Geo-Federated Cloud Data Centers." Procedia Computer Science 58 (2015): 151–57. http://dx.doi.org/10.1016/j.procs.2015.08.043.

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32

Nazir, Tawfeeq. "Impacts of Data Centres on the Environment." International Journal of Green Computing 5, no. 2 (July 2014): 1–12. http://dx.doi.org/10.4018/ijgc.2014070101.

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In the present era, the concept of virtualization is very common due to the advent of ICT. The data centers are the building blocks of IT business organizations providing the capabilities of centralized repository for storage, management, networking and dissemination of data. Continuous growth and demand in computational power endorsed for the creation of high scale data centers, requires very large amount of electrical power which acquires high operational cost and emission of carbon dioxide. These data centers not only consume a tremendous amount of energy but are riddled with IT inefficiencies and ill effects to the environment. This paper assesses to aggregate different impacts from different data centers, and so on. The future will bring increasing pressure for Data Center managers to use less electricity, buy “greener” equipment and take increased responsibility for the responsible management of e waste Power, Pollution and the Internet (2013).

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33

Ghose, Kanad, and Dale Becker. "Heterogeneous Integration for HPC and Data Centers." International Symposium on Microelectronics 2019, S2 (October 1, 2019): S1—S26. http://dx.doi.org/10.4071/2380-4505-2019.1.invitedhir000072.

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Abstract Invited Session on HETEROGENEOUS INTEGRATION ROADMAP - Heterogeneous Integration for HPC and Data Centers. This TWG focuses on the system-level implications related to performance, power management, security, power distribution issues and others

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34

Sumsuzoha, Md, MD Sohel Rana, Md Shahidul Islam, Md Khalilor Rahman, Mitu Karmakar, Md Sazzad Hossain, and Reza E. Rabbi Shawon. "LEVERAGING MACHINE LEARNING FOR RESOURCE OPTIMIZATION IN USA DATA CENTERS: A FOCUS ON INCOMPLETE DATA AND BUSINESS DEVELOPMENT." American Journal of Engineering and Technology 06, no. 12 (December 19, 2024): 119–40. https://doi.org/10.37547/tajet/volume06issue12-12.

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Data centers form the cornerstone of modern digital infrastructure, enabling operations from e-commerce and streaming to cloud computing and artificial intelligence. The United States, at the forefront of technology, houses some of the world's most extensive and technologically advanced data centers as a part of its economic and technological framework. This study aimed to explore how different ML techniques can be used for optimizing resource utilization by data centers in the US, focusing on strategies to handle incomplete data and implications for business development. The dataset was retrieved from the GitHub repository, which provided a rich dataset of resource usage metrics and operational data from U.S. data centers. It contained complex and fine-grained information that was necessary to optimize data center performance and deal with incomplete data challenges. A detailed description of the dataset and its key attributes was provided. It was designed for analyzing resource usage patterns in data centers, putting much emphasis on energy efficiency, workload distribution, and operational reliability. This integration of time-series data with sensor readings and performance logs provided a comprehensive overview of resource consumption and environmental conditions in data center operations. This dataset was curated for the engagement of machine learning models in the study and optimization of resource consumption along with the challenges of missing data. Analysis of resource utilization in US data centers was accomplished using the application of various models for machine learning, most notably, Logistic Regression, Random Forest, and Support Vector Machines; Retrospectively, the Random Forest and SVM models seem to be robust and reliable, placing the Random Forest slightly above, given their performance is nearly perfect for training and testing. The application of machine learning techniques holds huge potential for the reformation of resource management in US data centers. These models analyze a pattern in historical data to predict future resource demands, thus allowing optimized resource allotment and minimizing operational costs.

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35

Quinteros, Javier, Jerry A. Carter, Jonathan Schaeffer, Chad Trabant, and Helle A. Pedersen. "Exploring Approaches for Large Data in Seismology: User and Data Repository Perspectives." Seismological Research Letters 92, no. 3 (January 27, 2021): 1531–40. http://dx.doi.org/10.1785/0220200390.

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Abstract New data acquisition techniques are generating data at much finer temporal and spatial resolution, compared to traditional seismic experiments. This is a challenge for data centers and users. As the amount of data potentially flowing into data centers increases by one or two orders of magnitude, data management challenges are found throughout all stages of the data flow. The Incorporated Research Institutions for Seismology—Réseau sismologique et géodésique français and GEOForschungsNetz data centers—carried out a survey and conducted interviews of users working with very large datasets to understand their needs and expectations. One of the conclusions is that existing data formats and services are not well suited for users of large datasets. Data centers are exploring storage solutions, data formats, and data delivery options to meet large dataset user needs. New approaches will need to be discussed within the community, to establish large dataset standards and best practices, perhaps through participation of stakeholders and users in discussion groups and forums.

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36

Takci, Mehmet Turker, Tuba Gozel, and Mehmet Hakan Hocaoglu. "Quantitative Evaluation of Data Centers’ Participation in Demand Side Management." IEEE Access 9 (2021): 14883–96. http://dx.doi.org/10.1109/access.2021.3052204.

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37

Liu, Hui, AbdusSalam Aljbri, Jie Song, Jinqing Jiang, and Chun Hua. "Research advances on AI-powered thermal management for data centers." Tsinghua Science and Technology 27, no. 2 (April 2022): 303–14. http://dx.doi.org/10.26599/tst.2021.9010019.

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38

KumarRaghuwanshi, Anil, and Kavita Burse. "Capacity Management for Virtualized Data Centers using ECIES and Scheduling." International Journal of Computer Applications 103, no. 10 (October 18, 2014): 41–45. http://dx.doi.org/10.5120/18112-9283.

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39

Sharma, R. K., C. E. Bash, C. D. Patel, R. J. Friedrich, and J. S. Chase. "Balance of Power: Dynamic Thermal Management for Internet Data Centers." IEEE Internet Computing 9, no. 1 (January 2005): 42–49. http://dx.doi.org/10.1109/mic.2005.10.

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40

Liu, Zhenhua, Yuan Chen, Cullen Bash, Adam Wierman, Daniel Gmach, Zhikui Wang, Manish Marwah, and Chris Hyser. "Renewable and cooling aware workload management for sustainable data centers." ACM SIGMETRICS Performance Evaluation Review 40, no. 1 (June 7, 2012): 175–86. http://dx.doi.org/10.1145/2318857.2254779.

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41

Gao, Yongqiang, Haibing Guan, Zhengwei Qi, Bin Wang, and Liang Liu. "Quality of service aware power management for virtualized data centers." Journal of Systems Architecture 59, no. 4-5 (April 2013): 245–59. http://dx.doi.org/10.1016/j.sysarc.2013.03.007.

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42

Cioara, Tudor, Ionut Anghel, and Ioan Salomie. "Methodology for energy aware adaptive management of virtualized data centers." Energy Efficiency 10, no. 2 (August 11, 2016): 475–98. http://dx.doi.org/10.1007/s12053-016-9467-2.

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43

Zhang, Kai, Yiwen Zhang, Jinxiang Liu, and Xiaofeng Niu. "Recent advancements on thermal management and evaluation for data centers." Applied Thermal Engineering 142 (September 2018): 215–31. http://dx.doi.org/10.1016/j.applthermaleng.2018.07.004.

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44

Wang, Hongliang, and Daogui Tang. "Challenges and opportunities for the energy management of sustainable data centers in smart grids." IOP Conference Series: Earth and Environmental Science 984, no. 1 (February 1, 2022): 012005. http://dx.doi.org/10.1088/1755-1315/984/1/012005.

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Abstract With the increase of cloud computing and internet services, data centers are emerging to satisfy the requirement, leading to incremental energy consumption demand and emissions of green house gases. Thus, integration of renewable energies with the traditional power grid is preferred to reduce the environmental impact and increase energy efficiency, which lead to a demand of energy management strategies to coordinate the energy demand and generation. In this paper, we review the challenges for the sustainable data centers in smart grids with regards to energy management strategies, integration with renewable energies and cyber-attacks and propose possible solutions. Through the analysis of the data centers from the perspective of both smart grids level and micro-grid level, the research challenges and potential research directions in the energy management of sustainable data centers have been discussed.

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45

Pang, Shanchen, Weiguang Zhang, Tongmao Ma, and Qian Gao. "Ant Colony Optimization Algorithm to Dynamic Energy Management in Cloud Data Center." Mathematical Problems in Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/4810514.

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With the wide deployment of cloud computing data centers, the problems of power consumption have become increasingly prominent. The dynamic energy management problem in pursuit of energy-efficiency in cloud data centers is investigated. Specifically, a dynamic energy management system model for cloud data centers is built, and this system is composed of DVS Management Module, Load Balancing Module, and Task Scheduling Module. According to Task Scheduling Module, the scheduling process is analyzed by Stochastic Petri Net, and a task-oriented resource allocation method (LET-ACO) is proposed, which optimizes the running time of the system and the energy consumption by scheduling tasks. Simulation studies confirm the effectiveness of the proposed system model. And the simulation results also show that, compared to ACO, Min-Min, and RR scheduling strategy, the proposed LET-ACO method can save up to 28%, 31%, and 40% energy consumption while meeting performance constraints.

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46

Binesh, Fatemeh, and Saravanan Muthaiyah. "Dashboard for Assessing Data Centers Green Compliance." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 6, no. 1 (September 30, 2013): 719–26. http://dx.doi.org/10.24297/ijmit.v6i1.753.

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Abstract: Nowadays, ICT sector activities and in particular Data Centers are known as an important environmental hazard. With the increasing popularity of the Internet and cloud computing, this threat seems to even get worse in the near future. Despite this increasing importance, there is still little have been done about data centers environmental affects and in particular measuring their green compliance level including all three Rs of waste management (Reuse, Reuse and Recycle). This paper tries to introduce a dashboard for evaluating data centers level of green compliance regardless of their tier. However, the dashboard is proposed based on Malaysias data centers condition, it still can be beneficial to data center managers in other parts of the world and researchers to open up new research possibilities.

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Kohar, M. Yusuf. "Literatur Review Management Analysis of Medical Record Management System in Puskesmas." RADINKA JOURNAL OF HEALTH SCIENCE 1, no. 4 (April 30, 2024): 140–48. http://dx.doi.org/10.56778/rjhs.v1i4.244.

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The medical record management system at the community health center level is the same as the hospital medical record management system. This study aims to determine the management of the medical record system in health centers. This study is a literature review using methods of traditional review to seek, identify, evaluate, and interpret theoretical references relevant to the management of medical record systems in public health centers. The numbering system used in the health center is the Unit Numbering System, The medical record naming system uses a direct naming system, a centralized medical record document storage system at the health center, and a medical record document alignment system at the health center, namely a direct number alignment system and alignment system per region area. According to alphabetical order, assembly activities have not been carried out properly because there is no standard operating procedure for their implementation. The coding implementation at the community health centers did not use the ICD-10 book as a guideline for providing disease codes, the community health centers did not have a fixed retention schedule, so the retention and destruction of medical record documents at the community health centers had not been implemented. Conclusion: The numbering and naming system implemented at the community health centers is inappropriate, and the implementation of the medical record data processing system at the community health centers has not been optimal.

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Cepeda-Carrión, Ignacio, and Gabriel Cepeda-Carrion. "How public sport centers can improve the sport consumer experience." International Journal of Sports Marketing and Sponsorship 19, no. 3 (August 6, 2018): 350–67. http://dx.doi.org/10.1108/ijsms-02-2017-0008.

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Purpose The sport consumer experience is becoming an important aspect to sport center’s management. From this point, the purpose of this paper is to explore and examine the relationship between sport centers’ absorptive capacity and sport consumer experience, proposing that internal knowledge management processes act as mediators in this relationship. Design/methodology/approach The study offers empirical insights by applying the consistent PLS algorithm (PLSc-SEM) in an analysis of data from 156 sport centers in Andalusia (Spain) and a sample of 3,150 sport users from these sport centers. Findings The results demonstrate that a sport center’s absorptive capacity for external knowledge is crucial for enhancing the sport consumer experience and also that this effect requires additional knowledge management, such as the sport center’s knowledge storage and knowledge application. Practical implications The practical implication for sport center managers is that knowledge management processes have a very strong influence on the sport consumer experience, when they are managed in a sequential way. Originality/value The main value of this paper is draw conclusions using a study of sport managers and sport consumers to increase value experience of those ones.

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van Es, Karin, Daan van der Weijden, and Jeroen Bakker. "The multifaceted and situated data center imaginary of Dutch Twitter." Big Data & Society 10, no. 1 (January 2023): 205395172311550. http://dx.doi.org/10.1177/20539517231155064.

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Data centers are material structures that take up space, use resources like water and energy, and possess a large carbon footprint. This paper examines the broader long-term discussion around data centers during the period 2020–2022 in the Dutch Twittersphere. Through an analysis of tweets and images, it identifies and reflects on the communities active in the discussion and the range of visions and imaginaries of data centers they produce. Unpacking these tweets and images over time traces not only the emergence of a ‘reactive imaginary’, critical of the promises of information technology (IT) industry and (local) governments, but also the blind spots of the discussion. It furthermore reveals an important role for journalism in the discussion by questioning the claims of the industry and contributing to a ‘visibility expansion’ of data center’s impact on Earth's resources. The paper shows the multifaceted and situated nature of imaginaries and their role in shaping decision-making and policy.

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Al-Nefaiee, Abdullah H., Alia M. Alhaif, and Najah Algoblan. "Data Centers in the Kingdom of Saudi Arabia and their Role in Big Data Processing for Decision Support." Journal of Educational and Social Research 12, no. 2 (March 5, 2022): 237. http://dx.doi.org/10.36941/jesr-2022-0048.

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The present research paper aims to identify the role of data centers in big data processing for decision support, the sources of information in data centers in data processing, the competencies of data specialists for processing big data to promote decision support, as well as the difficulties encountered by data specialist in data centers during the processing of big data. The authors adopted the descriptive analytical approach by designing and applying a questionnaire to a sample of (313) employees in data, documentation, and decision support centers in the public and private sectors in Riyadh, Saudi Arabia. The results showed that data centers play a major role in big data processing. Most of the participants (62.1%) reported that feasibility studies are the most important source of decision support at the center. Moreover, (51.1%) of the participants received training courses in statistical processing. Some difficulties face data centers, including the lack of the quality, maturity, processing, and maintenance of data. The study recommends the need to securely and adequately update the management systems and programs of data centers and to provide specialized training courses for data center employees. Received: 11 November 2021 / Accepted: 28 January 2022 / Published: 5 March 2022

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