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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 (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 (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 (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 (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 (2014): 1033–45. http://dx.doi.org/10.1109/tcomm.2014.012614.130747.

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6

Kumar, Sanjay, Vanish Talwar, Vibhore Kumar, Parthasarathy Ranganathan, and Karsten Schwan. "Loosely coupled coordinated management in virtualized data centers." Cluster Computing 14, no. 3 (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 (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 (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 (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 cybe

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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 (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, m

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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 (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 (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

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15

Bhardwaj, Rakhi, R. Padmavathy, M. Preetha, et al. "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, mi

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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 (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 (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 (

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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 (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 (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 (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 (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

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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 (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 (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 (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 (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 (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 (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 t

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29

Guitart, Jordi. "Toward sustainable data centers: a comprehensive energy management strategy." Computing 99, no. 6 (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 (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

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31

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 (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 inefficienc

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33

Ghose, Kanad, and Dale Becker. "Heterogeneous Integration for HPC and Data Centers." International Symposium on Microelectronics 2019, S2 (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, et al. "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 (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 retri

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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 (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 datase

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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 (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 (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 (2005): 42–49. http://dx.doi.org/10.1109/mic.2005.10.

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40

Liu, Zhenhua, Yuan Chen, Cullen Bash, et al. "Renewable and cooling aware workload management for sustainable data centers." ACM SIGMETRICS Performance Evaluation Review 40, no. 1 (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 (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 (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 (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, int

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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, whi

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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 (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 the

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47

Kohar, M. Yusuf. "Literatur Review Management Analysis of Medical Record Management System in Puskesmas." RADINKA JOURNAL OF HEALTH SCIENCE 1, no. 4 (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 medi

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48

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 (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. Fin

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49

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 (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

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50

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 (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 Riya

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