Relevant bibliographies by topics / Data center

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data center'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 March 2025

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Journal articles on the topic "Data center"

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Srivastava, Ankit, and Jatin Nagpal. "Ideal Architecture for Security Operation Center Complementing Data Centers: An Overview." International Journal of Science and Research (IJSR) 10, no. 12 (December 27, 2021): 77–79. https://doi.org/10.21275/sr211130123410.

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T, Mr Ganesh, Dr Nirmal kumar A, and M. Gurubala. "Effective Bandwidth Assurance in Data Center." International Journal of Trend in Scientific Research and Development Special Issue, Special Issue-Active Galaxy (June 30, 2018): 65–69. http://dx.doi.org/10.31142/ijtsrd16966.

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Widyanto, Toto, and Widjojo Hardjoprakoso. "Greenship Data Center – A Green Data Centre Standard for Indonesia." Proceeding of the International Conference on Multidisciplinary Research for Sustainable Innovation 1 (August 12, 2024): 492–500. http://dx.doi.org/10.31098/icmrsi.v1i.836.

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Data centers are becoming increasingly important in today's digital age, as they offer a more sustainable and energy-efficient approach to managing and storing data. With the growing awareness of environmental impact and the need for sustainable practices, it is crucial to develop standardized rating tools for green data centers. This paper aims to provide a comprehensive overview of the development process of green data center standard rating tools, tailored to the unique context and challenges faced by Indonesia. The Greenship Data Center (DC) is developed by a collaboration between the Green Building Council Indonesia (GBC Indonesia) and IPUSTAH-ID (Ikatan Profesional Pusat Data Hijau Indonesia - Association of Indonesia Green Data Center Professionals). This paper also explores the key components and considerations for developing the Greenship DC. Furthermore, challenges and potential solutions will be addressed for implementing these rating tools in the Indonesian context.
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Rusdiana, Komang Ery, linawati linawati, and I. Made Oka Widyantara. "Green Data Center Design of Udayana University." Majalah Ilmiah Teknologi Elektro 15, no. 2 (December 15, 2016): 39–44. http://dx.doi.org/10.24843/mite.1502.07.

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Manda, Jeevan. "Data Center Optimization for Telecom: Implementing Cost-Effective Strategies to Optimize Data Center Operations in Telecom, Focusing on Efficiency Improvements and Cost Reduction Initiatives, Aligned with Your Data Center Optimization Expertise." International Journal of Science and Research (IJSR) 8, no. 3 (March 5, 2019): 1981–87. http://dx.doi.org/10.21275/sr24907112459.

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Daim, Tugrul, Jay Justice, Mark Krampits, Matthew Letts, Ganesh Subramanian, and Mukundan Thirumalai. "Data center metrics." Management of Environmental Quality: An International Journal 20, no. 6 (September 25, 2009): 712–31. http://dx.doi.org/10.1108/14777830910990870.

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Kant, Krishna. "Data center evolution." Computer Networks 53, no. 17 (December 2009): 2939–65. http://dx.doi.org/10.1016/j.comnet.2009.10.004.

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Makholm, Jeff D., and Laura T. W. Olive. "Data Center Problems." Climate and Energy 41, no. 4 (October 7, 2024): 21–26. http://dx.doi.org/10.1002/gas.22431.

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“Hyperscale” data centers accompanying the rapid growth of artificial intelligence (AI) is one of the fastest growing uses of electricity in the United States and the rest of the world. Indeed, such growth, with the retirement of dispatchable fossil fuel electricity plants, drives persistent warnings about potential reliability problems for the US power system. As Federal Energy Regulatory Commission's (FERC's) Commissioner Mark Christie warned in late July, to the House Subcommittee on Energy, Climate, and Grid Security.
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Santos, Alexandre F., Pedro D. Gaspar, and Heraldo J. L. de Souza. "New Data Center Performance Index: Perfect Design Data Center—PDD." Climate 8, no. 10 (October 4, 2020): 110. http://dx.doi.org/10.3390/cli8100110.

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Data Centers (DC) are specific buildings that require large infrastructures to store all the information needed by companies. All data transmitted over the network is stored on CDs. By the end of 2020, Data Centers will grow 53% worldwide. There are methodologies that measure the efficiency of energy consumption. The most used metric is the Power Usage Effectiveness (PUE) index, but it does not fully reflect efficiency. Three DC’s located at the cities of Curitiba, Londrina and Iguaçu Falls (Brazil) with close PUE values, are evaluated in this article using the Energy Usage Effectiveness Design (EUED) index as an alternative to the current method. EUED uses energy as a comparative element in the design phase. Infrastructure consumption is the sum of energy with Heating, Ventilating and Air conditioning (HVAC) equipment, equipment, lighting and others. The EUED values obtained were 1.245 (kWh/yr)/(kWh/yr), 1.313 (kWh/yr)/(kWh/yr) and 1.316 (kWh/yr)/(kWh/yr) to Curitiba, Londrina and Iguaçu Falls, respectively. The difference between the EUED and the PUE Constant External Air Temperature (COA) is 16.87% for Curitiba, 13.33% for Londrina and 13.30% for Iguaçu Falls. The new Perfect Design Data center (PDD) index prioritizes efficiency in increasing order is an easy index to interpret. It is a redefinition of EUED, given by a linear equation, which provides an approximate result and uses a classification table. It is a decision support index for the location of a Data Center in the project phase.
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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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Dissertations / Theses on the topic "Data center"

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Wiswell, Shane. "Data center migration." [Denver, Colo.] : Regis University, 2007. http://165.236.235.140/lib/SWiswell2007.pdf.

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Sehery, Wile Ali. "OneSwitch Data Center Architecture." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/94376.

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In the last two-decades data center networks have evolved to become a key element in improving levels of productivity and competitiveness for different types of organizations. Traditionally data center networks have been constructed with 3 layers of switches, Edge, Aggregation, and Core. Although this Three-Tier architecture has worked well in the past, it poses a number of challenges for current and future data centers. Data centers today have evolved to support dynamic resources such as virtual machines and storage volumes from any physical location within the data center. This has led to highly volatile and unpredictable traffic patterns. Also The emergence of "Big Data" applications that exchange large volumes of information have created large persistent flows that need to coexist with other traffic flows. The Three-Tier architecture and current routing schemes are no longer sufficient for achieving high bandwidth utilization. Data center networks should be built in a way where they can adequately support virtualization and cloud computing technologies. Data center networks should provide services such as, simplified provisioning, workload mobility, dynamic routing and load balancing, equidistant bandwidth and latency. As data center networks have evolved the Three-Tier architecture has proven to be a challenge not only in terms of complexity and cost, but it also falls short of supporting many new data center applications. In this work we propose OneSwitch: A switch architecture for the data center. OneSwitch is backward compatible with current Ethernet standards and uses an OpenFlow central controller, a Location Database, a DHCP Server, and a Routing Service to build an Ethernet fabric that appears as one switch to end devices. This allows the data center to use switches in scale-out topologies to support hosts in a plug and play manner as well as provide much needed services such as dynamic load balancing, intelligent routing, seamless mobility, equidistant bandwidth and latency.
PHD
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Sergejev, Ivan. "Exposing the Data Center." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/51838.

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Given the rapid growth in the importance of the Internet, data centers - the buildings that store information on the web - are quickly becoming the most critical infrastructural objects in the world. However, so far they have received very little, if any, architectural attention. This thesis proclaims data centers to be the 'churches' of the digital society and proposes a new type of a publicly accessible data center. The thesis starts with a brief overview of the history of data centers and the Internet in general, leading to a manifesto for making data centers into public facilities with an architecture of their own. After, the paper proposes a roadmap for the possible future development of the building type with suggestions for placing future data centers in urban environments, incorporating public programs as a part of the building program, and optimizing the inside workings of a typical data center. The final part of the work, concentrates on a design for an exemplary new data center, buildable with currently available technologies. This thesis aims to: 1) change the public perception of the internet as a non-physical thing, and data centers as purely functional infrastructural objects without any deeper cultural significance and 2) propose a new architectural language for the type.
Master of Architecture
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Wang, Qinjin. "Multi Data center Transaction Chain : Achieving ACID for cross data center multi-key transactions." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-198664.

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Transaction support for Geo-replicated storage system is one of the most popular challenges in the last few years. Some systems gave up for supporting transactions and let upper application layer to handle it. While some other systems tried with different solutions on guaranteeing the correctness of transactions and paid some efforts on performance improvements. However, there are very few systems that claim the supporting of ACID in the global scale. In this thesis, we have studied on various data consistency and transaction design theories such as Paxos, transaction chopping, transaction chain, etc. We have also analyzed several recent distributed transactional systems. As the result, a Geo-replicated transactional framework, namely Multi Data center Transaction Chain (MDTC), is designed and implemented. MDTC adopts transaction chopping approach, which brings more concurrency by chopping transactions into pieces. A two phase traversal mechanism is designed to validate and maintain dependencies. For cross data center consistency, a Paxos like majority vote protocol is designed and implemented as a state machine. Moreover, some tuning such as executing read-only transaction locally helps to improve performance of MDTC in different scenarios. MDTC only requires 1 cross data center message roundtrip for executing a distributed transaction globally. ACID properties are kept in MDTC. We have evaluated MDTC with an extended TPC-C benchmark on top of Cassandra. The results from various setups have been evaluated and the result shows that MDTC achieves a good performance on throughout and latency. Meanwhile it has very low abort rate and scales well for transactions executed in a global scale.
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Talarico, Gui. "Urban Data Center: A Architectural Celebration of Data." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/42855.

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Throughout the last century, the popularization of the automobile and development of roads and highways has changed the way we live, and how cities develop. Bridges, aqueducts, and power plants had comparable impact in the past. I consider each of these examples to be â iconsâ of infrastructures that we humans build to improve our living environments and to fulfill our urge to become better.Fast forward to now. The last decades showed us the development of new sophisticated networks that connect people and continents. Communication grids, satellite communication, high speed fiber optics and many other technologies have made possible the existence of the ultimate human network - the internet. A network created by us to satisfy our needs to connect, to share, to socialize and communicate over distances never before imagined. The data center is the icon of this network.Through modern digitalization methods, text, sounds, images, and knowledge can be converted into zeroâ s and oneâ s and distributed almost instantly to all corners of the world. The data center is the center piece in the storage, processing, and distribution of this data.The Urban Data Center hopes to bring this icon closer to its creators and users. Let us celebrate its existence and shed some light into the inner workings of the worldâ s largest network. Let the users that inhabit this critical network come inside of it and understand where it lives. This thesis explores the expressive potential of networks and data through the design of a data center in Washington, DC.
Master of Architecture
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Müller, Thomas. "Innovative Technologien im Data Center." Universitätsbibliothek Chemnitz, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200900947.

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Vorgestellt wird die Architektur des Data Centers der TU Chemnitz, die auf den neuen Technologie Data Center Bridging (DCB) und Fibre Channel over Ethernet (FCoE) basiert. Es werden die entsprechenden Standards dargestellt und ein Überblick zur gegenwärtig verfügbaren Technik gegeben. Das Rechenzentrum der TU Chemnitz setzt diese Technologien bereits erfolgreich im Kontext von VMware-Virtualisierung und bei Betrieb I/O-intensiver Systeme ein.
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Bjarnadóttir, Margrét Vilborg. "Data-driven approach to health care : applications using claims data." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45946.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2008.
Includes bibliographical references (p. 123-130).
Large population health insurance claims databases together with operations research and data mining methods have the potential of significantly impacting health care management. In this thesis we research how claims data can be utilized in three important areas of health care and medicine and apply our methods to a real claims database containing information of over two million health plan members. First, we develop forecasting models for health care costs that outperform previous results. Secondly, through examples we demonstrate how large-scale databases and advanced clustering algorithms can lead to discovery of medical knowledge. Lastly, we build a mathematical framework for a real-time drug surveillance system, and demonstrate with real data that side effects can be discovered faster than with the current post-marketing surveillance system.
by Margrét Vilborg Bjarnadóttir.
Ph.D.
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Le, Guen Thibault. "Data-driven pricing." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45627.

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Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2008.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 143-146).
In this thesis, we develop a pricing strategy that enables a firm to learn the behavior of its customers as well as optimize its profit in a monopolistic setting. The single product case as well as the multi product case are considered under different parametric forms of demand, whose parameters are unknown to the manager. For the linear demand case in the single product setting, our main contribution is an algorithm that guarantees almost sure convergence of the estimated demand parameters to the true parameters. Moreover, the pricing strategy is also asymptotically optimal. Simulations are run to study the sensitivity to different parameters.Using our results on the single product case, we extend the approach to the multi product case with linear demand. The pricing strategy we introduce is easy to implement and guarantees not only learning of the demand parameters but also maximization of the profit. Finally, other parametric forms of the demand are considered. A heuristic that can be used for many parametric forms of the demand is introduced, and is shown to have good performance in practice.
by Thibault Le Guen.
S.M.
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Javanshir, Marjan. "DC distribution system for data center." Thesis, Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B39344952.

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Bennion, Laird. "Identifying data center supply and demand." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/103457.

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Thesis: S.M. in Real Estate Development, Massachusetts Institute of Technology, Program in Real Estate Development in conjunction with the Center for Real Estate, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 66-69).
This thesis documents new methods for gauging supply and demand of data center capacity and addresses issues surrounding potential threats to data center demand. This document is divided between a primer on the composition and engineering of a current data center, discussion of issues surrounding data center demand, Moore's Law and cloud computing, and then transitions to presentation of research on data center demand and supply.
by Laird Bennion.
S.M. in Real Estate Development
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Books on the topic "Data center"

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Guo, Deke. Data Center Networking. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9368-7.

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Liu, Yang, Jogesh K. Muppala, Malathi Veeraraghavan, Dong Lin, and Mounir Hamdi. Data Center Networks. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01949-9.

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Geological Survey (U.S.). National Mapping Division, ed. EROS Data Center. [Washington, D.C.?]: Dept. of the Interior, U.S. Geological Survey, National Mapping Division, 1990.

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United States. Army. Corps of Engineers, ed. Navigation Data Center. [Fort Belvoir, Va.]: U.S. Army Corps of Engineers, 1993.

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Geological Survey (U.S.). National Mapping Division., ed. EROS Data Center. [Washington, D.C.?]: Dept. of the Interior, U.S. Geological Survey, National Mapping Division, 1990.

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Maurizio, Portolani, ed. Data center fundamentals. Indianapolis, Ind: Cisco, 2004.

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United States. Bureau of Alcohol, Tobacco, Firearms, and Explosives. Public Affairs Division. U.S. Bomb Data Center. Washington, D.C: Bureau of Alcohol, Tobacco, Firearms and Explosives, Public Affairs Division, 2008.

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United States. Bureau of the Census. State Data Center Program. [Washington, D.C.]: U.S. Dept. of Commerce, U.S. Census Bureau, 2001.

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Carter, Luther F. Data center consolidation study. [Columbia, S.C: State of South Carolina B&CB, 1997.

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Center, National Geophysical Data, and United States. National Oceanic and Atmospheric Administration, eds. National Geophysical Data Center. Boulder, CO (325 Broadway, Boulder 80303): The Center, 1996.

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Book chapters on the topic "Data center"

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Hogan, Mél. "Data Center." In Encyclopedia of Big Data, 272–75. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-32010-6_299.

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Hogan, Mél. "Data Center." In Encyclopedia of Big Data, 1–4. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-32001-4_299-1.

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Gough, Corey, Ian Steiner, and Winston Saunders. "Data Center Management." In Energy Efficient Servers, 307–18. Berkeley, CA: Apress, 2015. http://dx.doi.org/10.1007/978-1-4302-6638-9_9.

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Khan, Muhammad Usman Shahid, and Samee U. Khan. "Smart Data Center." In Handbook on Data Centers, 247–62. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2092-1_7.

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Süß, Ralf, and Yannik Süß. "Data Center Facilities." In IT Infrastructure, 15–31. Berkeley, CA: Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0077-1_2.

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Bhattacharya, Prajesh. "Data Center Monitoring." In Energy Efficient Thermal Management of Data Centers, 199–236. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-7124-1_5.

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Weik, Martin H. "data processing center." In Computer Science and Communications Dictionary, 354. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_4340.

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Rasool, Shaik, and Uma N. Dulhare. "Data Center Security." In Green Computing in Network Security, 53–77. New York: CRC Press, 2021. http://dx.doi.org/10.1201/9781003097198-4.

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Agarwal, Sonali, Murli Dhar Tiwari, and Iti Tiwari. "E Governance Data Center." In E Governance Data Center, Data Warehousing and Data Mining, 113–28. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003357254-6.

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Jew, Alexander. "Data Center Telecommunications Cabling." In Data Center Handbook, 257–74. Hoboken, NJ: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781118937563.ch14.

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Conference papers on the topic "Data center"

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Sedlak, Daniel, Michal Polak, Jan Fesl, and Pavel Tvrdik. "Data center network monitoring framework." In 2024 IEEE International Conference on Cloud Engineering (IC2E), 256–57. IEEE, 2024. http://dx.doi.org/10.1109/ic2e61754.2024.00039.

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Rahal-Arabi, Tawfik, Paul Van der Arend, Ashish Jain, Mehdi Saidi, Rashad Oreifej, Sriram Sundaram, Srilatha Manne, et al. "Optimizing GPU Data Center Power." In 2024 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), 358–62. IEEE, 2024. https://doi.org/10.1109/apccas62602.2024.10808935.

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Wang, Hao, and Yizhao Zhang. "Deterministic Data Center Network Calculus." In 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA), 1177–82. IEEE, 2024. https://doi.org/10.1109/ispa63168.2024.00157.

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Sun, Gang, Sitong Bu, Vishal Anand, Victor Chang, and Dan Liao. "Reliable Virtual Data Center Embedding Across Multiple Data Centers." In International Conference on Internet of Things and Big Data. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0005842101950203.

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Bhoja, Sudeep. "PAM4 Signaling for intra–data center and Data center to data center connectivity (DCI)." In Optical Fiber Communication Conference. Washington, D.C.: OSA, 2017. http://dx.doi.org/10.1364/ofc.2017.w4d.5.

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Devarakonda, Ranjeet, Giri Prakash, Kavya Guntupally, and Jitendra Kumar. "Big Federal Data Centers Implementing FAIR Data Principles: ARM Data Center Example." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006051.

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Snyder, William A., Herbert Gursky, Harry M. Heckathorn, Bob L. Lucke, Bryan N. Dorland, R. A. Kessel, S. L. Berg, and E. G. Dombrowski. "Backgrounds Data Center." In SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, edited by Robert E. Huffman and Christos G. Stergis. SPIE, 1994. http://dx.doi.org/10.1117/12.186615.

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Cao, Peirui, Shizhen Zhao, Min Yee The, Yunzhuo Liu, and Xinbing Wang. "TROD: Evolving From Electrical Data Center to Optical Data Center." In 2021 IEEE 29th International Conference on Network Protocols (ICNP). IEEE, 2021. http://dx.doi.org/10.1109/icnp52444.2021.9651977.

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Cader, Tahir, Levi Westra, and Andres Marquez. "Technologies for the Energy-Efficient Data Center." In ASME 2007 InterPACK Conference collocated with the ASME/JSME 2007 Thermal Engineering Heat Transfer Summer Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/ipack2007-33463.

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Although semiconductor manufacturers have provided temporary relief with lower-power multi-core microprocessors, OEMs and data center operators continue to push the limits for individual rack power densities. It is not uncommon today for data center operators to deploy multiple 20 kW racks in a facility. Such rack densities are exacerbating the major issues of power and cooling in data centers. Data center operators are now forced to take a hard look at the efficiencies of their data centers. Malone and Belady (2006) have proposed three metrics, i.e., Power Usage Effectiveness (PUE), Data Center Efficiency (DCE), and the Energy-to-Acquisition Cost ratio (EAC), to help data center operators quickly quantify the efficiency of their data centers. In their paper, Malone and Belady present nominal values of PUE across a broad cross-section of data centers. PUE values are presented for data centers at four levels of optimization. One of these optimizations involves the use of Computational Fluid Dynamics (CFD). In the current paper, CFD is used to conduct an in-depth investigation of a liquid-cooled data center that would potentially be housed at the Pacific Northwest National Labs (PNNL). The boundary conditions used in the CFD model are based upon actual measurements on a rack of liquid-cooled servers housed at PNNL. The analysis shows that the liquid-cooled facility could achieve a PUE of 1.57 as compared to a PUE of 3.0 for a typical data center (the lower the PUE, the better, with values below 1.6 approaching ideal). The increase in data center efficiency is also translated into an increase in the amount of IT equipment that can be deployed. At a PUE of 1.57, the analysis shows that 91% more IT equipment can be deployed as compared to the typical data center. The paper will discuss the analysis of the PUE, and will also explore the impact of the raising data center efficiency via the use of multiple cooling technologies and CFD analysis. Complete results of the analyses will be presented in the paper.
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Zheng, Wenli, and Xiaorui Wang. "Data Center Sprinting: Enabling Computational Sprinting at the Data Center Level." In 2015 IEEE 35th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2015. http://dx.doi.org/10.1109/icdcs.2015.26.

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Reports on the topic "Data center"

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Bensley, S., D. Thaler, P. Balasubramanian, L. Eggert, and G. Judd. Data Center TCP (DCTCP): TCP Congestion Control for Data Centers. RFC Editor, October 2017. http://dx.doi.org/10.17487/rfc8257.

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Meyers, H. World Data Center system. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1994. http://dx.doi.org/10.4095/193875.

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Avramov, L., and J. Rapp. Data Center Benchmarking Terminology. RFC Editor, August 2017. http://dx.doi.org/10.17487/rfc8238.

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Avramov, L., and J. Rapp. Data Center Benchmarking Methodology. RFC Editor, August 2017. http://dx.doi.org/10.17487/rfc8239.

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Critchlow, T. J., L. Liu, C. Pu, A. Gupta, B. Ludaescher, I. Altintas, M. Vouk, D. Bitzer, M. Singh, and D. Rosnick. Scientific Data Management Center Scientific Data Integration. Office of Scientific and Technical Information (OSTI), January 2003. http://dx.doi.org/10.2172/15003250.

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Shoshani, Arie. The Scientific Data Management Center. Office of Scientific and Technical Information (OSTI), June 2006. http://dx.doi.org/10.2172/886956.

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Kemper, Toni, and Jack Briscoe. National Training Center Data Library. Fort Belvoir, VA: Defense Technical Information Center, January 1987. http://dx.doi.org/10.21236/ada178947.

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Rezanka, Deborah. LANL HPC Data Center Monitoring. Office of Scientific and Technical Information (OSTI), February 2022. http://dx.doi.org/10.2172/1845247.

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Xu, Tengfang, and Steve Greenberg. Data Center Energy Benchmarking: Part 5 - Case Studies on aCorporate Data Center (No. 22). Office of Scientific and Technical Information (OSTI), August 2007. http://dx.doi.org/10.2172/926604.

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GLIMM, J. THE CENTER FOR DATA INTENSIVE COMPUTING. Office of Scientific and Technical Information (OSTI), November 2001. http://dx.doi.org/10.2172/15007518.

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