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Статті в журналах з теми "Data Cloud center"
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.
Повний текст джерелаKaramat Khan, Tehmina, Mohsin Tanveer, and Asadullah Shah. "Energy Efficiency in Virtualized Data Center." International Journal of Engineering & Technology 7, no. 4.15 (October 7, 2018): 315. http://dx.doi.org/10.14419/ijet.v7i4.15.23019.
Повний текст джерелаRajput, 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.
Повний текст джерелаYang, Jing Bo, Shu Huang, and Pan Jiang. "Research on Distributed Heterogeneous Data Storage Algorithm in Cloud Computing Data Center." Applied Mechanics and Materials 624 (August 2014): 553–56. http://dx.doi.org/10.4028/www.scientific.net/amm.624.553.
Повний текст джерелаKanniga Devi R., Murugaboopathi Gurusamy, and Vijayakumar P. "An Efficient Cloud Data Center Allocation to the Source of Requests." Journal of Organizational and End User Computing 32, no. 3 (July 2020): 23–36. http://dx.doi.org/10.4018/joeuc.2020070103.
Повний текст джерелаBao, Hao. "Homomorphic computing of encrypted data outsourcing in cloud data center." Frontiers in Computing and Intelligent Systems 2, no. 1 (November 23, 2022): 1–3. http://dx.doi.org/10.54097/fcis.v2i1.2482.
Повний текст джерелаWang, Yi Nuo. "Research and Design of Enterprise-Class Network Data Center Based on Cloud Computing." Applied Mechanics and Materials 651-653 (September 2014): 1893–95. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.1893.
Повний текст джерелаGovindasamy, C., and J. Venkatesh. "Data Center Management-Cloud Migration Techniques." Asian Journal of Research in Business Economics and Management 7, no. 10 (2017): 1. http://dx.doi.org/10.5958/2249-7307.2017.00174.8.
Повний текст джерелаFiliposka, Sonja, and Carlos Juiz. "Community-based complex cloud data center." Physica A: Statistical Mechanics and its Applications 419 (February 2015): 356–72. http://dx.doi.org/10.1016/j.physa.2014.10.017.
Повний текст джерелаVolkova, Elena Viktorovna, Anzhelika Andreevna Kostornaya, and Ruslana Aleksandrovna Amikishieva. "THE DETERMINATION OF CLOUD COVER PARAMETERS USING SATELLITE DATA PROCESSING SYSTEMS." Географический вестник = Geographical bulletin, no. 3(54) (2020): 124–34. http://dx.doi.org/10.17072/2079-7877-2020-3-124-134.
Повний текст джерелаДисертації з теми "Data Cloud center"
Sergejev, Ivan. "Exposing the Data Center." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/51838.
Повний текст джерелаMaster of Architecture
Zhuang, Hao. "Performance Evaluation of Virtualization in Cloud Data Center." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-104206.
Повний текст джерелаAmazon Elastic Compute Cloud (EC2) har antagits av ett stort antal små och medelstora företag (SMB), t.ex. foursquare, Monster World, och Netflix, för att ge olika typer av tjänster. Det finns en del tidigare arbeten i den aktuella litteraturen som undersöker variationen och oförutsägbarheten av molntjänster. Dessa arbetenhar visat intressanta iakttagelser om molnerbjudanden, men de har misslyckats med att avslöja den underliggande kärnan hos de olika utseendena för molntjänster. I denna avhandling tittade vi på de underliggande schemaläggningsmekanismerna och maskinvarukonfigurationer i Amazon EC2, och undersökte deras inverkan på resultatet för de virtuella maskiners instanser som körs ovanpå. Närmare bestämt är det flera fall med standard- och hög-CPU instanser som omfattas att belysa uppgradering av hårdvara och utbyte av Amazon EC2. Stora instanser från standardfamiljen är valda för att genomföra en fokusanalys. För att bättre förstå olika beteenden av de olika instanserna har lokala kluster miljöer inrättas, dessa klustermiljöer består av två Intel Xeonservrar och har inrättats med hjälp av olika schemaläggningsalgoritmer. Genom en serie benchmarkmätningar observerade vi följande slutsatser: (1) Amazon använder mycket diversifierad hårdvara för att tillhandahållandet olika instanser. Från de olika instans-sub-typernas perspektiv leder hårdvarumångfald till betydande prestationsvariation som kan nå upp till 30%. (2) Två olika schemaläggningsmekanismer observerades, en liknande Simple Earliest Deadline Fist(SEDF) schemaläggare, medan den andra mer liknar Credit-schemaläggaren i Xenhypervisor. Dessa två schemaläggningsmekanismer ger även upphov till variationer i prestanda. (3) Genom att tillämpa en enkel "trial-and-failure" strategi för val av instans, är kostnadsbesparande förvånansvärt stor. Med tanke på fördelning av snabba och långsamma instanser kan kostnadsbesparingen uppgå till 30%, vilket är attraktivt för små och medelstora företag som använder Amazon EC2 plattform.
Tudoran, Radu-Marius. "High-Performance Big Data Management Across Cloud Data Centers." Electronic Thesis or Diss., Rennes, École normale supérieure, 2014. http://www.theses.fr/2014ENSR0004.
Повний текст джерелаThe easily accessible computing power offered by cloud infrastructures, coupled with the "Big Data" revolution, are increasing the scale and speed at which data analysis is performed. Cloud computing resources for compute and storage are spread across multiple data centers around the world. Enabling fast data transfers becomes especially important in scientific applications where moving the processing close to data is expensive or even impossible. The main objectives of this thesis are to analyze how clouds can become "Big Data - friendly", and what are the best options to provide data management services able to meet the needs of applications. In this thesis, we present our contributions to improve the performance of data management for applications running on several geographically distributed data centers. We start with aspects concerning the scale of data processing on a site, and continue with the development of MapReduce type solutions allowing the distribution of calculations between several centers. Then, we present a transfer service architecture that optimizes the cost-performance ratio of transfers. This service is operated in the context of real-time data streaming between cloud data centers. Finally, we study the viability, for a cloud provider, of the solution consisting in integrating this architecture as a service based on a flexible pricing paradigm, qualified as "Transfer-as-a-Service"
de, Carvalho Tiago Filipe Rodrigues. "Integrated Approach to Dynamic and Distributed Cloud Data Center Management." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/739.
Повний текст джерелаRaad, Patrick. "Protocol architecture and algorithms for distributed data center networks." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066571/document.
Повний текст джерелаWhile many business and personal applications are being pushed to the cloud, offering a reliable and a stable network connectivity to cloud-hosted services becomes an important challenge to face in future networks. In this dissertation, we design advanced network protocols, algorithms and communication strategies to cope with this evolution in distributed data center architectures. We propose a user-centric distributed cloud network architecture that is able to: (i) migrate virtual resources between data centers with an optimized service downtime; (ii) offer resilient access to virtual resources; (iii) minimize the cloud access latency. We identify two main decision making problems: the virtual machine orchestration problem, also taking care of user mobility, and the routing locator switching configuration problem, taking care of both extra and intra data center link states. We evaluate our architecture using real test beds of geographically distributed data centers, and we also simulate realistic scenarios based on real mobility traces. We show that migrating virtual machines between data centers at negligible downtime is possible by enhancing overlay protocols. We then demonstrate that by linking cloud virtual resource mobility to user mobility we can get a considerable gain in the transfer rates. We prove by simulations using real traces that the virtual machine placement decision is more important than the routing locator switching decision problem when the goal is to increase the connection throughput: the cloud access performance is primarily affected by the former decision, while the latter decision can be left to intra data center traffic engineering solutions. Finally, we propose solutions to take profit from multipath transport protocols for accelerating cloud access performance in our architecture, and to let link-state intra data center routing fabrics piloting the cloud access routing locator switching
Pipkin, Everest R. "It Was Raining in the Data Center." Research Showcase @ CMU, 2018. http://repository.cmu.edu/theses/138.
Повний текст джерелаMahmud, A. S. M. Hasan. "Sustainable Resource Management for Cloud Data Centers." FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/2634.
Повний текст джерелаLi, Dawei. "On the Design and Analysis of Cloud Data Center Network Architectures." Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/413608.
Повний текст джерелаPh.D.
Cloud computing has become pervasive in the IT world, as well as in our daily lives. The underlying infrastructures for cloud computing are the cloud data centers. The Data Center Network (DCN) defines what networking devices are used and how different devices are interconnected in a cloud data center; thus, it has great impacts on the total cost, performances, and power consumption of the entire data center. Conventional DCNs use tree-based architectures, where a limited number of high-end switches and high-bandwidth links are used at the core and aggregation levels to provide required bandwidth capacity. A conventional DCN often suffers from high expenses and low fault-tolerance, because high-end switches are expensive and a failure of such a high-end switch will result in disastrous consequences in the network. To avoid the problems and drawbacks in conventional DCNs, recent works adopt an important design principle: using Commodity-Off-The-Shelf (COTS) cheap switches to scale out data centers to large sizes, instead of using high-end switches to scale up data centers. Based on this scale-out principle, a large number of novel DCN architectures have been proposed. These DCN architectures are classified into two categories: switch-centric and server-centric DCN architectures. In both switch-centric and server-centric architectures, COTS switches are used to scale out the network to a large size. In switch-centric DCNs, routing intelligence is placed on switches; each server usually uses only one port of the Network Interface Card (NIC) to connect to the switches. In server-centric DCNs, switches are only used as dummy cross-bars; servers in the network serve as both computation nodes and packet forwarding nodes that connect switches and other servers, and routing intelligence is placed on servers, where multiple NIC ports may be used. This dissertation considers two fundamental problems in designing DCN architectures using the scale-out principle. The first problem considers how to maximize the total number of dual-port servers in a server-centric DCN given a network diameter constraint. Motivated by the Moore Bound, which provides the upper bound on the number of nodes in a traditional graph given a node degree and diameter, we give an upper bound on the maximum number of dual-port servers in a DCN, given a network diameter constraint and a switch port number. Then, we propose three novel DCN architectures, SWCube, SWKautz, and SWdBruijn, whose numbers of servers are close to the upper bound, and are larger than existing DCN architectures in most cases. SWCube is based on the generalized hypercube. SWCube accommodates a comparable number of servers to that of DPillar, which is the largest existing one prior to our work. SWKautz and SWdBruijn are based on the Kautz graph and the de Bruijn graph, respectively. They always accommodate more servers than DPillar. We investigate various properties of SWCube, SWKautz, and SWdBruijn; we also compare them with various existing DCN architectures and demonstrate their advantages over existing architectures. The second problem focuses on the tradeoffs between network performances and power consumption in designing DCN architectures. We have two motivations for our work. The first one is that most existing works take extreme designs in terms of improving network performances and reducing the power consumption. Some DCNs use too many networking devices to improve the performances; their power consumption is very high. Other DCNs use two few networking devices, and their performances are very poor. We are interested in exploring the quantitative tradeoffs between network performances and power consumption in designing DCN architectures. The second motivation is that there do not exist important unified performance and power consumption metrics for general DCNs. Thus, we propose two important unified performance and power consumption metrics. Then, we propose three novel DCN architectures that achieve important tradeoff points in the design spectrum: FCell, FSquare, and FRectangle. Besides, we find that in all these three new architectures, routing intelligence can be placed on both servers and switches; thus they enjoy the advantages of both switch-centric and server-centric architectures, and can be regarded as a new category of DCN architectures, the dual-centric DCN architectures. We also investigate various other properties for our proposed architectures and verify that they are excellent candidates for practical cloud data centers.
Temple University--Theses
Soares, Maria José. "Data center - a importância de uma arquitectura." Master's thesis, Universidade de Évora, 2011. http://hdl.handle.net/10174/11604.
Повний текст джерелаIzumo, Naoki. "Clouded space: Internet physicality." Thesis, University of Iowa, 2017. https://ir.uiowa.edu/etd/5515.
Повний текст джерелаКниги з теми "Data Cloud center"
Zhang, Lei, and Le Chen. Cloud Data Center Network Architectures and Technologies. First edition. | Boca Raton : CRC Press, 2021. | Summary: “This book has: CRC Press, 2021. http://dx.doi.org/10.1201/9781003143185.
Повний текст джерелаMalcolm, Orr, and Page Greg, eds. Cloud computing: Automating the virtualized data center. Indianapolis, IN: Cisco Press, 2012.
Знайти повний текст джерелаTsai, Linjiun, and Wanjiun Liao. Virtualized Cloud Data Center Networks: Issues in Resource Management. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32632-0.
Повний текст джерела1960-, Franklin Curtis, ed. Cloud computing: Technologies and strategies of the ubiquitous data center. New York: CRC, 2010.
Знайти повний текст джерелаChee, Brian J. S. Yun ji suan: Wu chu bu zai de shu ju zhong xin = Cloud computing ; technologies and strategies of the ubiquitous data center. Beijing: Guo fang gong ye chu ban she, 2013.
Знайти повний текст джерелаInternational Business Machines Corporation. International Technical Support Organization, ed. Managing security and compliance in cloud or virtualized data centers. [Poughkeepsie, NY: IBM Corp., International Technical Support Organization], 2013.
Знайти повний текст джерелаK, Kokula Krishna Hari, ed. An Efficient Load Balancing Algorithm for virtualized Cloud Data Centers: ICCCEG 2014. Vietnam: Association of Scientists, Developers and Faculties, 2014.
Знайти повний текст джерелаBeard, Haley. Cloud computing best practices: For managing and measuring processes for on-demand computing, applications and data centers in the cloud with SLAs. Brisbane, Australia: Art of Service, 2008.
Знайти повний текст джерелаUnited States. National Aeronautics and Space Administration., ed. Reduction and analysis of seasons 15 and 16 (1991-1992), Pioneer Venus radio occultation data and correlative studies with observations of the near infra-red emission of Venus: Report to the National Aeronautics and Space Administration, Ames Research Center for grant NCC2-753, April 1, 1992 through May 31, 1995. [Washington, DC: National Aeronautics and Space Administration, 1995.
Знайти повний текст джерелаGreen computing: Tools and techniques for saving energy, money, and resources. Boca Raton: CRC Press, 2014.
Знайти повний текст джерелаЧастини книг з теми "Data Cloud center"
Comer, Douglas E. "Data Center Infrastructure And Equipment." In The Cloud Computing Book, 37–52. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003147503-6.
Повний текст джерелаCherkaoui, Omar, and Ramesh Menon. "Virtualization, Cloud, SDN, and SDDC in Data Centers." In Data Center Handbook, 389–400. Hoboken, NJ: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781118937563.ch20.
Повний текст джерелаZheng, Jianping, Yue Sun, and Wenhui Zhou. "Cloud Computing Based Internet Data Center." In Lecture Notes in Computer Science, 700–704. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10665-1_75.
Повний текст джерелаQi, Jiaju, and Long Zhao. "Data Center Network in Cloud Computing." In Encyclopedia of Wireless Networks, 260–63. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-78262-1_322.
Повний текст джерелаQi, Jiaju, and Long Zhao. "Data Center Network in Cloud Computing." In Encyclopedia of Wireless Networks, 1–4. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-32903-1_322-1.
Повний текст джерелаTsai, Linjiun, and Wanjiun Liao. "Transformation of Data Center Networks." In Virtualized Cloud Data Center Networks: Issues in Resource Management., 15–27. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32632-0_3.
Повний текст джерелаLin, Xiuyan, Zhanghui Liu, and Wenzhong Guo. "Energy-Efficient VM Placement Algorithms for Cloud Data Center." In Cloud Computing and Big Data, 42–54. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28430-9_4.
Повний текст джерелаKantarci, B., and H. T. Mouftah. "Inter-Data-Center Networks with Minimum Operational Costs." In Cloud Services, Networking, and Management, 105–28. Hoboken, NJ: John Wiley & Sons, Inc, 2015. http://dx.doi.org/10.1002/9781119042655.ch5.
Повний текст джерелаSvoboda, Tomas, and Josef Horalek. "Utilization of NFV in Cloud Data Center." In Lecture Notes in Electrical Engineering, 156–62. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75605-9_22.
Повний текст джерелаAlhebaishi, Nawaf, Lingyu Wang, Sushil Jajodia, and Anoop Singhal. "Threat Modeling for Cloud Data Center Infrastructures." In Foundations and Practice of Security, 302–19. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51966-1_20.
Повний текст джерелаТези доповідей конференцій з теми "Data Cloud center"
Sengupta, Sudipta. "Cloud data center networks." In the ACM SIGMETRICS joint international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1993744.1993782.
Повний текст джерелаSydney, Ali, Abdul Alim, Chris Ward, Claude Basso, and Bengi Karacali. "Cloud Data Center Fabric Virtualization." In 2022 IEEE 15th International Conference on Cloud Computing (CLOUD). IEEE, 2022. http://dx.doi.org/10.1109/cloud55607.2022.00048.
Повний текст джерелаFiandrino, Claudio, Dzmitry Kliazovich, Pascal Bouvry, and Albert Y. Zomaya. "Performance Metrics for Data Center Communication Systems." In 2015 IEEE 8th International Conference on Cloud Computing (CLOUD). IEEE, 2015. http://dx.doi.org/10.1109/cloud.2015.23.
Повний текст джерелаHuang, Lei, Qin Jia, Xin Wang, Shuang Yang, and Baochun Li. "PCube: Improving Power Efficiency in Data Center Networks." In 2011 IEEE 4th International Conference on Cloud Computing (CLOUD). IEEE, 2011. http://dx.doi.org/10.1109/cloud.2011.74.
Повний текст джерелаMengistu, Tessema, Abdulrahman Alahmadi, Abdullah Albuali, Yousef Alsenani, and Dunren Che. "A "No Data Center" Solution to Cloud Computing." In 2017 IEEE 10th International Conference on Cloud Computing (CLOUD). IEEE, 2017. http://dx.doi.org/10.1109/cloud.2017.99.
Повний текст джерелаS K, Shravan, J. Lakshmi, and Neeraj Bisht. "Towards Improving Data Center Utilisation by Reducing Fragmentation." In 2018 IEEE 11th International Conference on Cloud Computing (CLOUD). IEEE, 2018. http://dx.doi.org/10.1109/cloud.2018.00141.
Повний текст джерелаHu, Yan, Ming Zhu, Yong Xia, Kai Chen, and Yanlin Luo. "GARDEN: Generic Addressing and Routing for Data Center Networks." In 2012 IEEE 5th International Conference on Cloud Computing (CLOUD). IEEE, 2012. http://dx.doi.org/10.1109/cloud.2012.9.
Повний текст джерелаYuan, Haitao, Jing Bi, Jia Zhang, Wei Tan, and Keman Huang. "Workload-Aware Revenue Maximization in SDN-Enabled Data Center." In 2017 IEEE 10th International Conference on Cloud Computing (CLOUD). IEEE, 2017. http://dx.doi.org/10.1109/cloud.2017.12.
Повний текст джерелаSaed, Kamariah Abu, Norshakirah Aziz, Ade Wahyu Ramadhani, and Noor Hafizah Hassan. "Data Governance Cloud Security Assessment at Data Center." In 2018 4th International Conference on Computer and Information Sciences (ICCOINS). IEEE, 2018. http://dx.doi.org/10.1109/iccoins.2018.8510612.
Повний текст джерелаDutreilh, Xavier, Aurélien Moreau, Jacques Malenfant, Nicolas Rivierre, and Isis Truck. "From Data Center Resource Allocation to Control Theory and Back." In 2010 IEEE International Conference on Cloud Computing (CLOUD). IEEE, 2010. http://dx.doi.org/10.1109/cloud.2010.55.
Повний текст джерелаЗвіти організацій з теми "Data Cloud center"
DEFENSE BUSINESS BOARD WASHINGTON DC. DoD Information Technology Modernization: A Recommended Approach to Data Center Consolidation and Cloud Computing. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada563977.
Повний текст джерелаGurieiev, Viktor, Yulii Kutsan, Anna Iatsyshyn, Andrii Iatsyshyn, Valeriia Kovach, Evgen Lysenko, Volodymyr Artemchuk, and Oleksandr Popov. Simulating Systems for Advanced Training and Professional Development of Energy Specialists in Power Sector. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4456.
Повний текст джерела