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Journal articles on the topic 'Grid Computing. Unvollkommene Information'

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Published: 4 June 2021
Last updated: 5 February 2022

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1

Fischbach, Kai. "Grid computing." WIRTSCHAFTSINFORMATIK 48, no. 6 (December 2006): 452. http://dx.doi.org/10.1007/s11576-006-0101-3.

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Mohammad Khanli, L., and M. Analoui. "Active Grid Information Server for grid computing." Journal of Supercomputing 50, no. 1 (November 11, 2008): 19–35. http://dx.doi.org/10.1007/s11227-008-0250-9.

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Bry, François, Wolfgang E. Nagel, and Michael Schroeder. "Grid-Computing." Informatik-Spektrum 27, no. 6 (December 2004): 542–45. http://dx.doi.org/10.1007/s00287-004-0443-4.

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4

Edwards, C. "Getting to grips with grid (grid computing)." Information Professional 3, no. 1 (February 1, 2006): 14–18. http://dx.doi.org/10.1049/inp:20060101.

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Abbadi, Imad M., and Muntaha Alawneh. "Secure information sharing for grid computing." Security and Communication Networks 3, no. 6 (December 2, 2009): 486–502. http://dx.doi.org/10.1002/sec.157.

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6

Schneider, Marcel. "Grid-Computing — Science oder Fiction?" WIRTSCHAFTSINFORMATIK 48, no. 1 (January 2006): 75–76. http://dx.doi.org/10.1007/s11576-006-0016-z.

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7

Wang, Lizhe, Gregor von Laszewski, Dan Chen, Jie Tao, and Marcel Kunze. "Provide Virtual Machine Information for Grid Computing." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 40, no. 6 (November 2010): 1362–74. http://dx.doi.org/10.1109/tsmca.2010.2052598.

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8

Yang, Guangwen, Hai Jin, Minglu Li, Nong Xiao, Wei Li, Zhaohui Wu, Yongwei Wu, and Feilong Tang. "Grid Computing in China." Journal of Grid Computing 2, no. 2 (June 2004): 193–206. http://dx.doi.org/10.1007/s10723-004-4201-2.

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Zhang, Juheng, Subhajyoti Bandyopadhyay, and Selwyn Piramuthu. "Real option valuation on grid computing." Decision Support Systems 46, no. 1 (December 2008): 333–43. http://dx.doi.org/10.1016/j.dss.2008.07.003.

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Purdam, Kingsley, Mark Elliot, Stephen Pickles, and Duncan Smith. "GRID COMPUTING AND DISCLOSURE CONTROL." New Review of Information Networking 10, no. 2 (November 2004): 161–76. http://dx.doi.org/10.1080/13614570500053908.

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11

Opitz, Alek, Hartmut König, and Sebastian Szamlewska. "What Does Grid Computing Cost?" Journal of Grid Computing 6, no. 4 (February 16, 2008): 385–97. http://dx.doi.org/10.1007/s10723-008-9098-8.

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12

Qin, Yu Yang, and Xi Fu Wang. "Research on Applications of Grid Technology in Railway Information Service." Applied Mechanics and Materials 587-589 (July 2014): 2120–23. http://dx.doi.org/10.4028/www.scientific.net/amm.587-589.2120.

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In view of the new requirement of railway information service, such as information sharing, collaboration, etc. which occurred during the development of railway information construction, Grid computing was introduced into the field of railway information service, which analyzed the development trend of railway information service based on grid, made a research on the application of the grid computing in the field of railway, constructed railway application framework based on grid, proposed grid-based railway IT system and provided a theoretical basis and reference for the grid application in railway information service.
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Xue, Yong, Jianqin Wang, Yanguang Wang, Chaolin Wu, and Yincui Hu. "Preliminary study of Grid computing for remotely sensed information." International Journal of Remote Sensing 26, no. 16 (August 20, 2005): 3613–30. http://dx.doi.org/10.1080/01431160500077028.

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14

Leinberger, W., and V. Kumar. "Information power grid: The new frontier in parallel computing?" IEEE Concurrency 7, no. 4 (October 1999): 75–84. http://dx.doi.org/10.1109/mcc.1999.806982.

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Kumar, Dr Vinod, Er Gagandeep Raheja, and Ms Jyoti Sodhi. "CLOUD COMPUTING." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 1 (February 1, 2013): 5–7. http://dx.doi.org/10.24297/ijct.v4i1a.3025.

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Cloud computing is the delivery of computing as a service rather than a product, whereby shared resources, software, and information are provided to computers and other devices as a utility (like the electricity grid) over a network (typically the internet). Cloud computing provides computation, software, data access, and storage services that do not require end-user knowledge of the physical location and configuration of the system that delivers the services. Parallel to this concept can be drawn with the electricity grid, wherein end-users consume power without needing to understand the component devices or infrastructure required to provide the service.
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16

Geddes, Neil. "The Large Hadron Collider and Grid computing." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 370, no. 1961 (February 28, 2012): 965–77. http://dx.doi.org/10.1098/rsta.2011.0465.

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We present a brief history of the beginnings, development and achievements of the worldwide Large Hadron Collider Computing Grid (wLCG). The wLCG is a huge international endeavour, which is itself embedded within, and directly influences, a much broader computing and information technology landscape. It is often impossible to identify true cause and effect, and they may appear very different from the different perspectives (e.g. information technology industry or academic researcher). This account is no different. It represents a personal view of the developments over the last two decades and is therefore inevitably biased towards those things in which the author has been personally involved.
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17

Fan, Bang Kui, Guan Jun Ding, Teng Long, Hai Bin Lan, and Jing Wang. "Study on the Cloud Computing Architecture for Smart Grid Application." Advanced Materials Research 805-806 (September 2013): 1073–77. http://dx.doi.org/10.4028/www.scientific.net/amr.805-806.1073.

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In view of the increasing demands of data storage, information process, interoperability and so on in smart grid construction, this paper presents the cloud computing architecture for smart grid application, based on introducing of the cloud computing technology which has strong capability of distributed computing and storage. And the aim of the paper is to overcome the drawbacks of traditional power grid information platform in computation, storage, information process, etc., and promote the smart grid development. First of all, the essential connotation and performance demands of smart grid are described. Second, the development process and main properties of cloud computing are summarized in the paper. Then, the feasibility of constructing cloud computing architecture for smart grid application is analyzed and discussed. Based on that, the structure composition of the architecture is proposed, and the main functions of each part are discussed in detail. It shows that the architecture can effectively implement amalgamation and interoperability among physical layer, information process and application system, as well as the power energy, information stream and business flow in smart grid are highly integrated, which can offer support for smart grid construction. Finally, the related conclusions are drawn, and some future research directions are given.
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18

Ahuja, Sanjay P., and Jack R. Myers. "A Survey on Wireless Grid Computing." Journal of Supercomputing 37, no. 1 (July 2006): 3–21. http://dx.doi.org/10.1007/s11227-006-3845-z.

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19

Taylor, Nolan J. "Public grid computing participation: An exploratory study of determinants." Information & Management 44, no. 1 (January 2007): 12–21. http://dx.doi.org/10.1016/j.im.2006.05.004.

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20

Krašovec, Barbara, and Andrej Filipčič. "Enhancing the Grid with Cloud Computing." Journal of Grid Computing 17, no. 1 (January 7, 2019): 119–35. http://dx.doi.org/10.1007/s10723-018-09472-w.

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21

Kumar, Arun, Neeran Karnik, and Vikas Agarwal. "Usage Metering for Service-Oriented Grid Computing." International Journal of E-Business Research 2, no. 1 (January 2006): 78–106. http://dx.doi.org/10.4018/jebr.2006010105.

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22

Mehmood, M. Yasir, Ammar Oad, Muhammad Abrar, Hafiz Mudassir Munir, Syed Faraz Hasan, H. Abd ul Muqeet, and Noorbakhsh Amiri Golilarz. "Edge Computing for IoT-Enabled Smart Grid." Security and Communication Networks 2021 (July 13, 2021): 1–16. http://dx.doi.org/10.1155/2021/5524025.

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Smart grid is a new vision of the conventional power grid to integrate green and renewable technologies. Smart grid (SG) has become a hot research topic with the development of new technologies, such as IoT, edge computing, artificial intelligence, big data, 5G, and so on. The efficiency of SG will be increased by smart embedded devices that have intelligent decision-making ability. Various types of sensors and data sources will collect data of high resolution. One of the vital challenges for IoT is to manage a large amount of data produced by sensors. Sending this massive amount of data directly to the cloud will create problems of latency, security, privacy, and high bandwidth utilization. This issue is addressed by edge computing (EC). In EC, the data are processed at the edge of the network that is near the embedded gadgets. This paper provides a comprehensive review of the smart grid systems, based on IoT and EC. The development in the rising technologies, the framework for EC-IoT-based SG, and requirements to implement the EC-IoT-based SG system are highlighted in the paper. Framework for EC-IoT-based SG is examined, and important requirements to implement the EC-IoT-based SG system are outlined. Finally, some critical issues and challenges faced in the implementation of EC-IoT-based SG systems are identified. Some important open research issues are also identified.
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23

Yan, Shu. "Intelligent Job Service Scheduling Scheme in Campus Grids." Advanced Materials Research 143-144 (October 2010): 462–66. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.462.

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Campus grid is to use grid technology, the existing campus network, distributed, heterogeneous computing resources and information resources to the virtual integrated together to form a pool of available computing resources and can provide information platform for users to query information. This paper presents job scheduling model based on open grid service architecture by analyzing the current status of campus network and grid technologies. The simulation results show that the models are feasibility and validity.
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24

Krol, Dariusz, and Grzegorz Kukla. "Reusable grid computing architecture based on code propagation method." International Journal of Intelligent Information and Database Systems 3, no. 1 (2009): 44. http://dx.doi.org/10.1504/ijiids.2009.023037.

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25

Town, Chris, and Karl Harrison. "Large‐scale grid computing for content‐based image retrieval." Aslib Proceedings 62, no. 4/5 (July 8, 2010): 438–46. http://dx.doi.org/10.1108/00012531011074681.

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26

Zhou, Guo Hong. "Study of Cloud Computing and Advanced Manufacturing Technology under Information Technology Development." Applied Mechanics and Materials 484-485 (January 2014): 227–30. http://dx.doi.org/10.4028/www.scientific.net/amm.484-485.227.

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Cloud computing can be regarded as the integration of grid computing and virtualization technology: namely the use of grid distributed computing processing power of IT resources and build into a resource pool, plus on a mature server virtualization, storage virtualization technology make users can real-time monitoring and deployment of resources. Cloud computing and advanced manufacturing technology is facing many challenges during the process of development in the future, including security and privacy will become a primary issue. This study focused on relations with the important areas of emerging industries, the fast scheduling network resources in the cloud computing and advanced manufacturing technology key issues, as well as cloud computing security management issues related to cloud computing and advanced manufacturing technology.
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27

Fielding, Nigel G. "Grid Computing and Qualitative Social Science." Social Science Computer Review 26, no. 3 (December 3, 2007): 301–16. http://dx.doi.org/10.1177/0894439307310822.

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28

You, Meihong, Wenping Luo, and Meizhang He. "Resource scheduling of information platform for general grid computing framework." International Journal of Web and Grid Services 16, no. 3 (2020): 254. http://dx.doi.org/10.1504/ijwgs.2020.10031644.

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29

You, Meihong, Wenping Luo, and Meizhang He. "Resource scheduling of information platform for general grid computing framework." International Journal of Web and Grid Services 16, no. 3 (2020): 254. http://dx.doi.org/10.1504/ijwgs.2020.109469.

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30

Han, Xu Li, Ye Qing Ren, and Xin Ru Liu. "Grid Generation Based on Symmetric Blend Functions of Boundary Information." Applied Mechanics and Materials 58-60 (June 2011): 2207–12. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2207.

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Based on the knowledge of the grid curves on the boundary curves, we propose a simple grid generation method by direct computing of grid curves. A kind of symmetric blend functions is given. The expressions ofu-directional grid curves andv-directional grid curves with shape parameters are constructed. By choosing different shape parameters, different grid curves can be obtained. Some numerical experiments show that the presented method is effective.
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31

Hao, Xu. "E-Commerce Model Based on Grid Computing." Applied Mechanics and Materials 34-35 (October 2010): 1190–94. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.1190.

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Recently, the emergence of the Internet, particularly the Web, has led to a growth in the use of e-commerce in business-to-business, business-to-consumer, business-to-public administration, consumer-to-public administration, and user-to-user activities, leading to new business opportunities, as well as to a variety of problems. This paper research presents a brief overview of e-commerce technology and utilizes a survey, based on this overview to garner insights from Information Systems professionals at leading e-commerce firms.
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32

Cody, Erin, Raj Sharman, Raghav H. Rao, and Shambhu Upadhyaya. "Security in grid computing: A review and synthesis." Decision Support Systems 44, no. 4 (March 2008): 749–64. http://dx.doi.org/10.1016/j.dss.2007.09.007.

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33

Joseph, J., M. Ernest, and C. Fellenstein. "Evolution of grid computing architecture and grid adoption models." IBM Systems Journal 43, no. 4 (2004): 624–45. http://dx.doi.org/10.1147/sj.434.0624.

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34

Anisenkov, Alexey, Julia Andreeva, Alessandro Di Girolamo, Panos Paparrigopoulos, and Aresh Vedaee. "CRIC: a unified information system for WLCG and beyond." EPJ Web of Conferences 214 (2019): 03003. http://dx.doi.org/10.1051/epjconf/201921403003.

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The Worldwide LHC Computing Grid (WLCG) is an innovative distributed environment which is deployed through the use of grid computing technologiesin order to provide computing and storage resources to the LHC experimentsfor data processing and physics analysis. Following increasing demands of LHC computing needs toward high luminosity era, the experiments are engagdin an ambitious program to extend the capability of WLCG distributed environment, for instance including opportunistically used resources such as High-Performance Computers (HPCs), cloud platforms and volunteer computer. norder to be effectively used by the LHC experiments, all these diverse distributed resources should be described in detail. This implies easy service discovery of shared physical resources, detailed description of service configurations and experiment-specific data structures is needed. In this contribution, we present a high-level information component of a distributed computing environment, the Computing Resource Information Catalogue (CRIC) which aims to facilitate distributed computing operations for the LHC experiments and consolidate WLCG topology information. In addition, CRIC performs data validation and provides coherent view and topology descriptinto the LHC VOs for service discovery and configuration. CRIC represents teevolution of ATLAS Grid Information System (AGIS) into the common experiment independent high-level information framework. CRIC’s mission is to serve not just ATLAS Collaboration needs for the description of the distributed environment but any other virtual organization relying on large scale distributed infrastructure as well as the WLCG on the global scope. The contribution describes CRIC architecture, implementation of data model,collectors, user interfaces, advanced authentication and access control components of the system.
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35

Mustafee, Navonil. "Exploiting grid computing, desktop grids and cloud computing for e‐science." Transforming Government: People, Process and Policy 4, no. 4 (October 12, 2010): 288–98. http://dx.doi.org/10.1108/17506161011081291.

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36

Yu, Jia, and Rajkumar Buyya. "A taxonomy of scientific workflow systems for grid computing." ACM SIGMOD Record 34, no. 3 (September 2005): 44–49. http://dx.doi.org/10.1145/1084805.1084814.

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37

Field, Laurence, Markus W. Schulz, and Maria Alandes Pradillo. "Grid Information Systems: Past, Present and Future." EPJ Web of Conferences 245 (2020): 03008. http://dx.doi.org/10.1051/epjconf/202024503008.

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Grid information systems enable the discovery of resources in a grid computing infrastructure and provide further information about their structure and state. The original concepts for a grid information system were defined over 20 years ago and the GLUE 2.0 information model specification was published 10 years ago. This contribution describes the current status and highlights the changes over the years. It provides an overview of today’s usage from analysing the system logs and compares this with results from over a decade ago. A critical analysis of the system is provided with lessons learned and some perspectives for the future.
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38

Kurzyniec, Dawid, Magdalena Sławińska, Jarosław Sławiński, and Vaidy Sunderam. "Unibus: a contrarian approach to grid computing." Journal of Supercomputing 42, no. 1 (March 9, 2007): 125–44. http://dx.doi.org/10.1007/s11227-006-0033-0.

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39

Sabamoniri, Saeed, and Alireza Souri. "A weighted resource discovery approach in grid computing." International Journal of Pervasive Computing and Communications 15, no. 3/4 (November 4, 2019): 199–223. http://dx.doi.org/10.1108/ijpcc-04-2019-0036.

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Purpose Grid computing is an effective environment for the execution of parallel applications that requires great computing power. This paper aims to present, based on the hierarchical architecture, an improved weighted resource discovery (WRD) algorithm to manage allocation of resources and minimize cost of communications between grid nodes. Design/methodology/approach A behavioral modeling method is addressed to prove the proposed method correctness. The behavioral model of the proposed algorithm is implemented by StarUML tool with two different model-checking mechanisms. Then, the resource discovery correctness is analyzed in terms of reachability condition, fairness condition and deadlock-free using NuSMV model checker. Findings The results show that WRD algorithm has better performance in requiring re-discovery process, the number of examined nodes in each request and discovering the free resources with high-bandwidth links. Originality/value To store information of resources, a new data structure called resource information table is proposed which facilitates resource finding of the algorithm. A behavioral modeling method is addressed to prove the proposed method correctness.
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40

Wang, Wei, Hui Yan Wang, Shen Jie Jia, and Shi Min Wei. "Rescue Robot Navigation in Grid Computing Environment." Advanced Materials Research 267 (June 2011): 848–51. http://dx.doi.org/10.4028/www.scientific.net/amr.267.848.

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To obtain the optimal path in a unknown disaster field,a rescue robot needs to build an environment map. The information of the disaster field is collected by the sonsors of different robots, all signal from sensors (mounted on all robots and signal form GPS) are sent to the bakeside parllel processors with wireless network. A grid computing environment serves as the backside parallel processors with Globus Toolkit, the grid computing processor process all the signals and construct the global map to help robot for navigation path planning.The rescue robot get control signal from the grid computing processor with wireless network,thus, the robot is not necessary to be sophisticated. New computing methods are given for parallel algorithm on grid environment. The navigation control is implemented with the cooperation among heterogeneous agents, the advantages of large seale computing on grid are shown.
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41

Chen, Aijun, Liping Di, Yuqi Bai, Yaxing Wei, and Yang Liu. "Grid computing enhances standards-compatible geospatial catalogue service." Computers & Geosciences 36, no. 4 (April 2010): 411–21. http://dx.doi.org/10.1016/j.cageo.2009.09.006.

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42

Gao, Guo Hong, Li Jun Xu, and Xian Feng Yang. "P2P Technology Application and Research of Grid Computing." Applied Mechanics and Materials 34-35 (October 2010): 937–41. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.937.

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Grid computing is a new type of network computing platform emerged after the World Wide Web, which objective is to provide a kind of infrastructure that shares all kinds of information for user． This paper brings forward a Grid-P2P model based on Peer-to-Peer computing, and designs a peer-to-peer genetic scheduling algorithm. The simulation results show that this model increased validity and accuracy of grid computing. It can be applied to the optimization of task scheduling successfully.
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43

Gu, Yunhong, and Robert Grossman. "SABUL: A Transport Protocol for Grid Computing." Journal of Grid Computing 1, no. 4 (2003): 377–86. http://dx.doi.org/10.1023/b:grid.0000037553.18581.3b.

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44

Chen, Aijun, Liping Di, Yaxing Wei, Yuqi Bai, and Yang Liu. "Use of grid computing for modeling virtual geospatial products." International Journal of Geographical Information Science 23, no. 5 (May 2009): 581–604. http://dx.doi.org/10.1080/13658810902733666.

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45

Ransbotham, Sam, Ishwar Murthy, Sabyasachi Mitra, and Sridhar Narasimhan. "Sequential Grid Computing: Models and Computational Experiments." INFORMS Journal on Computing 23, no. 2 (May 2011): 174–88. http://dx.doi.org/10.1287/ijoc.1100.0392.

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46

Yingchun, Hou. "The Data Transmission Network and Computing Environment of Spatial Information Grid." Procedia Engineering 15 (2011): 1931–36. http://dx.doi.org/10.1016/j.proeng.2011.08.360.

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47

Bashir, MohammedBakri, MuhammadShafie Abd Latiff, AboamamaAtahar Ahmed, Adil Yousif, and ManhalElfadil Eltayeeb. "Content-based Information Retrieval Techniques Based on Grid Computing: A Review." IETE Technical Review 30, no. 3 (2013): 223. http://dx.doi.org/10.4103/0256-4602.113511.

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48

Wei, Xian Min. "Grid-Based Web Information Retrieval Architecture Professional Content-Oriented." Applied Mechanics and Materials 39 (November 2010): 103–6. http://dx.doi.org/10.4028/www.scientific.net/amm.39.103.

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By the use of grid computing, cluster systems, XML and other technologies, distributed Web information resources were organized and managed effectively by professional content and region from the logic, to design grid-based Web information retrieval architecture for professional content, to provide one way.for the user efficient access to information resources for professional content.
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CHEN, Jian-gang, Ru-chuan WANG, and Hai-yan WANG. "The extended RBAC model based on grid computing." Journal of China Universities of Posts and Telecommunications 13, no. 3 (September 2006): 93–97. http://dx.doi.org/10.1016/s1005-8885(07)60019-3.

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50

XIAO, Peng, and Zhi-gang HU. "Relaxed resource advance reservation policy in grid computing." Journal of China Universities of Posts and Telecommunications 16, no. 2 (April 2009): 108–13. http://dx.doi.org/10.1016/s1005-8885(08)60213-7.

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