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Journal articles on the topic 'Grid computing'

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Published: 4 June 2021
Last updated: 17 February 2024

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1

Rakib, Abdur. "Grid Computing Introduction." Journal of Advances and Scholarly Researches in Allied Education 15, no. 5 (July 1, 2018): 140–44. http://dx.doi.org/10.29070/15/57603.

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2

Coveney, Peter V. "Scientific Grid computing." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 363, no. 1833 (July 18, 2005): 1707–13. http://dx.doi.org/10.1098/rsta.2005.1632.

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We introduce a definition of Grid computing which is adhered to throughout this Theme Issue. We compare the evolution of the World Wide Web with current aspirations for Grid computing and indicate areas that need further research and development before a generally usable Grid infrastructure becomes available. We discuss work that has been done in order to make scientific Grid computing a viable proposition, including the building of Grids, middleware developments, computational steering and visualization. We review science that has been enabled by contemporary computational Grids, and associated progress made through the widening availability of high performance computing.
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3

Sadiku, Matthew N. O., Adebowale E. Shadare, and Sarhan M. Musa. "Grid Computing." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 6 (June 30, 2017): 5–6. http://dx.doi.org/10.23956/ijarcsse/v7i6/01612.

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4

KISHIMOTO, Mitsuhiro, and Keisuke FUKUI. "Grid Computing." Journal of The Institute of Electrical Engineers of Japan 125, no. 7 (2005): 417–20. http://dx.doi.org/10.1541/ieejjournal.125.417.

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5

Dumitru, Ileana. "Grid Computing." International Journal of Sustainable Economies Management 3, no. 3 (July 2014): 67–78. http://dx.doi.org/10.4018/ijsem.2014070105.

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To calculate the potential impact of grid on the enterprise, one just needs to look back a decade or so ago. Those who remember how LANs developed in company years before the Web was born can easily get a picture of how Grid Computing in corporations may change in the years to come. In the early days of the Internet, there was a strong opposition to linking computers together in a network. Ultimately, however, the Internet has become a ubiquitous tool, and many experts predict the same outcome for Grid Computing on the enterprise. There are still concerns to consider and obstacles to be overcome, but the momentum behind corporate Grid Computing is quickly gathering pace. The integration of Grid Computing technologies into enterprise computing systems can provide a much richer range of possibilities. This integration should provide enhanced capabilities and interoperability to meet current virtual organization demands.
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6

Deelman, Ewa, and Carl Kesselman. "Grid Computing." Scientific Programming 10, no. 2 (2002): 101–2. http://dx.doi.org/10.1155/2002/368025.

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7

Rana, O. "Grid computing." Computer Bulletin 44, no. 6 (November 1, 2002): 28. http://dx.doi.org/10.1093/combul/44.6.28.

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8

Rajaraman, V. "Grid computing." Resonance 21, no. 5 (May 2016): 401–15. http://dx.doi.org/10.1007/s12045-016-0344-2.

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9

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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10

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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11

Ramakrishnan, L. "Grid computing - Securing next-generation grids." IT Professional 6, no. 2 (March 2004): 34–39. http://dx.doi.org/10.1109/mitp.2004.1278860.

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12

Vachhani, Prof Milan Kantilal, and Dr Kishor H. Atkotiya. "Similarities and Contrast between Grid Computing and Cloud Computing." Indian Journal of Applied Research 3, no. 3 (October 1, 2011): 54–56. http://dx.doi.org/10.15373/2249555x/mar2013/19.

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13

Yang, Chao-Tung, Wen-Jen Hu, and Kuan-Chou Lai. "On Construction of a Multi-Grid Resource Selection Strategy on Grids." International Journal of Grid and High Performance Computing 6, no. 1 (January 2014): 38–62. http://dx.doi.org/10.4018/ijghpc.2014010103.

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Grid computing is now in widespread use, integrating geographical computing resources across multiple virtual organizations to achieve high performance computing. A single grid does not often provide a vast resource because virtual organizations have inadequate computing resource restrictions for management on an organizational scale. This paper presents a new grid architecture named Multi-Grid, which integrates multiple computational grids from different virtual organizations. This study builds a resource broker on multiple grid environments, integrating a number of single grids from different virtual organizations without the limit of organizations. The purpose of the multiple-grid resource is to avoid wasting resources. In addition, this study proposes a Multi-Grid Resource Selection Strategy (MRGSS) for the resource broker to better allocate resources before submitting jobs, to avoid network congestion that consequently causes a decrease in performance.
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14

Fox, G. "Grid computing environments." Computing in Science & Engineering 5, no. 2 (March 2003): 68–72. http://dx.doi.org/10.1109/mcise.2003.1182964.

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15

Iosup, Alexandru, and Dick Epema. "Grid Computing Workloads." IEEE Internet Computing 15, no. 2 (March 2011): 19–26. http://dx.doi.org/10.1109/mic.2010.130.

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16

Strong, Paul. "Enterprise Grid Computing." Queue 3, no. 6 (July 2005): 50–59. http://dx.doi.org/10.1145/1080862.1080877.

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17

da Fontoura Costa, L., G. Travieso, and C. A. Ruggiero. "Complex grid computing." European Physical Journal B 44, no. 1 (March 2005): 119–28. http://dx.doi.org/10.1140/epjb/e2005-00107-6.

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18

Osborne, Ian. "Grid computing now." ITNOW 48, no. 4 (July 1, 2006): 32–33. http://dx.doi.org/10.1093/itnow/bwl042.

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19

Nwobodo, Ikechukwu. "Cloud Computing: A Detailed Relationship to Grid and Cluster Computing." International Journal of Future Computer and Communication 4, no. 2 (April 2015): 82–87. http://dx.doi.org/10.7763/ijfcc.2015.v4.361.

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20

S. Murali, S. Murali, C. B. Selvalakshmi C. B. Selvalakshmi, S. Padmadevi S. Padmadevi, and P. N. Karthikayan P. N. Karthikayan. "Data Mining Patters in Grid Computing." International Journal of Scientific Research 2, no. 3 (June 1, 2012): 137–38. http://dx.doi.org/10.15373/22778179/mar2013/43.

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21

Knight, W. "Unlocking the grid [grid computing technology]." Engineering & Technology 1, no. 3 (June 1, 2006): 42–45. http://dx.doi.org/10.1049/et:20060304.

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22

Gagliardi, F., and F. Grey. "Old world, new grid [grid computing]." IEEE Spectrum 43, no. 7 (July 2006): 28–33. http://dx.doi.org/10.1109/mspec.2006.1653002.

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23

Marsh, P. "Get on the grid [grid computing]." Computing and Control Engineering 16, no. 1 (February 1, 2005): 41–45. http://dx.doi.org/10.1049/cce:20050110.

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24

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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25

Thilmany, Jean. "Getting on the Grid." Mechanical Engineering 125, no. 03 (March 1, 2003): 46–48. http://dx.doi.org/10.1115/1.2003-mar-1.

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This review discusses grid computing that is a low-cost way to harness the central processing units of a group of workstations. The grid can be made up of any number of central processing units (CPU), and they may be far-flung or within the same company, or even in the same department. Grid computing puts to work on the grid all available CPUs at idle workstations and thus does away with the need for powerful servers or supercomputers. Sun Microsystems Inc., Santa Clara, CA, and IBM of Armonk, New York, have both released software within the past three years that can divide and farm out pieces of an application to several thousand linked computers. Microsoft is developing grid-computing software for use with its products, as are Hewlett-Packard, Palo Alto, CA, and others. Grid software is written in Linux, the open-standards operating system. However, because no gatekeeping technology is currently in place for grids, the hard work of IT managers seeking to implement grid technology comes when negotiating policies among departments and setting up grids accordingly.
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26

Aloisio, Giovanni, Massimo Cafaro, Euro Blasi, and Italo Epicoco. "The Grid Resource Broker, A Ubiquitous Grid Computing Framework." Scientific Programming 10, no. 2 (2002): 113–19. http://dx.doi.org/10.1155/2002/969307.

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Portals to computational/data grids provide the scientific community with a friendly environment in order to solve large-scale computational problems. The Grid Resource Broker (GRB) is a grid portal that allows trusted users to create and handle computational/data grids on the fly exploiting a simple and friendly web-based GUI. GRB provides location-transparent secure access to Globus services, automatic discovery of resources matching the user's criteria, selection and scheduling on behalf of the user. Moreover, users are not required to learn Globus and they do not need to write specialized code or to rewrite their existing legacy codes. We describe GRB architecture, its components and current GRB features addressing the main differences between our approach and related work in the area.
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27

Yang, Sen, and Ruijuan Zheng. "Edge computing Helps the Development of Smart Grid." Journal of Computing and Electronic Information Management 10, no. 3 (May 24, 2023): 69–71. http://dx.doi.org/10.54097/jceim.v10i3.8704.

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With the rapid development of smart grids, the number of terminals connected to them has sharply increased. Using these terminals, the smart grid management system can monitor real-time information such as power consumption and power quality in the power grid to ensure safe and stable operation of the power grid. However, the access of a large number of terminals has also increased the amount of data that needs to be processed in the power grid. The traditional centralized cloud computing model can no longer meet the needs of rapid data processing, and a new model is urgently needed to solve the problem. As an emerging distributed computing model, edge computing is an ideal solution to realize large-scale distributed management of smart terminals in the future smart grid by providing computing and storage capabilities at the network edge near device terminals and user sides, reducing data transmission delay, improving data reliability, protecting user privacy, and reducing the computing and storage pressure of cloud data centers.
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28

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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29

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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30

Patel, Farheen. "Comparative Study of Grid and Cloud Computing." International Journal of Scientific Research 3, no. 7 (June 1, 2012): 80–81. http://dx.doi.org/10.15373/22778179/july2014/28.

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31

Kaur, Simranjeet. "Enhancing the Techniques to Secure Grid Computing." International Journal of Trend in Scientific Research and Development Volume-1, Issue-6 (October 31, 2017): 460–63. http://dx.doi.org/10.31142/ijtsrd2531.

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32

Aloisio, Giovanni, and Domenico Talia. "Grid Computing: Towards a New Computing Infrastructure." Future Generation Computer Systems 18, no. 8 (October 2002): v—vi. http://dx.doi.org/10.1016/s0167-739x(02)00079-1.

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33

Casanova, Henri. "Distributed computing research issues in grid computing." ACM SIGACT News 33, no. 3 (September 2002): 50–70. http://dx.doi.org/10.1145/582475.582486.

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34

Sun, Yong, Wei Qing Yang, Ji Rong Xue, Jian Hui Tian, and Wen Wei Li. "Comparing of Cloud Computing and Grid Computing." Applied Mechanics and Materials 556-562 (May 2014): 5408–12. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5408.

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As the current hot technology, many large companies have launched their own cloud computing projects, and the ways to realize each project is different, and ultimately lead to a fragmented state to explain the concept of cloud computing today. Almost all manufacturers do not deny that cloud computing is follow-up and development of grid computing, but their philosophy is different. This article aims to explore the similarities and differences between grid computing and cloud computing, from features, technology and operation costs, and to explore the solution of technical problems related to outstanding solutions through grid technology in the development of cloud computing.
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35

Hey, T. "Computing grid unlocks research." Computer Bulletin 44, no. 4 (July 1, 2002): 18–19. http://dx.doi.org/10.1093/combul/44.4.18.

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36

Menasce, D. A., and E. Casalicchio. "QoS in grid computing." IEEE Internet Computing 8, no. 4 (July 2004): 85–87. http://dx.doi.org/10.1109/mic.2004.24.

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37

Lathia, M. "Advantages of Grid Computing." IEEE Distributed Systems Online 6, no. 2 (February 2005): 5. http://dx.doi.org/10.1109/mdso.2005.7.

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38

Goth, Greg. "Grid Computing Gets Small." IEEE Distributed Systems Online 7, no. 11 (November 2006): 3. http://dx.doi.org/10.1109/mdso.2006.66.

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39

Snavely, Allan, Greg Chun, Henri Casanova, Rob F. Van der Wijngaart, and Michael A. Frumkin. "Benchmarks for grid computing." ACM SIGMETRICS Performance Evaluation Review 30, no. 4 (March 2003): 27–32. http://dx.doi.org/10.1145/773056.773062.

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40

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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41

Shi, Zhongzhi, He Huang, Jiewen Luo, Fen Lin, and Haijun Zhang. "Agent-based grid computing." Applied Mathematical Modelling 30, no. 7 (July 2006): 629–40. http://dx.doi.org/10.1016/j.apm.2005.06.018.

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42

Chadwick, David. "Authorisation in Grid computing." Information Security Technical Report 10, no. 1 (January 2005): 33–40. http://dx.doi.org/10.1016/j.istr.2004.11.004.

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43

Schwiegelshohn, Uwe, Rosa M. Badia, Marian Bubak, Marco Danelutto, Schahram Dustdar, Fabrizio Gagliardi, Alfred Geiger, et al. "Perspectives on grid computing." Future Generation Computer Systems 26, no. 8 (October 2010): 1104–15. http://dx.doi.org/10.1016/j.future.2010.05.010.

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44

Wang, Hong Bo, and Zhong Wei Wang. "How to Realize the Heterogeneous Data Sharing through Grid Technology." Advanced Materials Research 314-316 (August 2011): 2037–41. http://dx.doi.org/10.4028/www.scientific.net/amr.314-316.2037.

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Grid technology has emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications, and, in some cases, high-performance orientation. To date, grid technology has been effectively put to use in academic and research institutions to power high-performance and technical computing applications. Grid technology is increasingly being viewed as the next phase of distributed technology. Built on pervasive Internet standards, grid technology enables organizations to share computing and information resources across department and organizational boundaries in a secure, highly efficient manner. Organizations around the world are utilizing grid technology today in such diverse areas as collaborative scientific research, drug discovery, financial risk analysis, and product design. Grid technology enables research-oriented organizations to solve problems that were infeasible to solve due to computing and data-integration constraints. Grids also reduce costs through automation and improved IT resource utilization. Finally, grid technology can increase an organization’s agility enabling more efficient business processes and greater responsiveness to change.
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45

Muntean, Ioan Lucian, and Alexandra Ioana Badiu. "Application Plugins for Distributed Simulations on the Grid." International Journal of Computers Communications & Control 6, no. 4 (December 1, 2011): 701. http://dx.doi.org/10.15837/ijccc.2011.4.2096.

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Computing grids are today still underexploited by scientific computing communities. The main reasons for this are, on the one hand, the complexity and variety of tools and services existent in the grid middleware ecosystem, and, on the other hand, the complexity of the development of applications capable to exploit the grids. We address in this work the challenge of developing grid applications that keep pace with the rapid evolution of grid middleware. For that, we propose an approach based on plugins for grid applications that encapsulate a set of commonly used type of grid operations. We further propose more complex high-level functionalities, such as the plugins for remote exploration of simulation scenarios and for monitoring of the behavior of end-user applications in grids. We provide an example of a grid application constructed with these software components and evaluate based on it the performance of our approach in the context of the simulation of biological neurons. The results obtained on test and production grids demonstrate the usefulness of the proposed plugins, with a small performance overhead compared to traditional grid tools.
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46

XHAFA, FATOS, JAVIER CARRETERO, LEONARD BAROLLI, and ARJAN DURRESI. "REQUIREMENTS FOR AN EVENT-BASED SIMULATION PACKAGE FOR GRID SYSTEMS." Journal of Interconnection Networks 08, no. 02 (June 2007): 163–78. http://dx.doi.org/10.1142/s0219265907001965.

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In this paper we present a study on the requirements for the design and implementation of simulation packages for Grid systems. Grids are emerging as new distributed computing systems whose main objective is to manage and allocate geographically distributed computing resources to applications and users in an efficient and transparent manner. Grid systems are at present very difficult and complex to use for experimental studies of large-scale distributed applications. Although the field of simulation of distributed computing systems is mature, recent developments in large-scale distributed systems are raising needs not present in the simulation of the traditional distributed systems. Motivated by this, we present in this work a set of basic requirements that any simulation package for Grid computing should offer. This set of functionalities is obtained after a careful review of most important existing Grid simulation packages and includes new requirements not considered in such simulation packages. Based on the identified set of requirements, a Grid simulator is developed and exemplified for the Grid scheduling problem.
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47

Meddeber, Meriem, and Belabbas Yagoubi. "Dynamic Dependent Tasks Assignment for Grid Computing." International Journal of Grid and High Performance Computing 3, no. 2 (April 2011): 44–58. http://dx.doi.org/10.4018/jghpc.2011040104.

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A computational grid is a widespread computing environment that provides huge computational power for large-scale distributed applications. One of the most important issues in such an environment is resource management. Task assignment as a part of resource management has a considerable effect on the grid middleware performance. In grid computing, task execution time is dependent on the machine to which it is assigned, and task precedence constraints are represented by a directed acyclic graph. This paper proposes a hybrid assignment strategy of dependent tasks in Grids which integrate static and dynamic assignment technologies. Grid computing is considered a set of clusters formed by a set of computing elements and a cluster manager. The main objective is to arrive at a method of task assignment that could achieve minimum response time and reduce the transfer cost, inducing by the tasks transfer respecting the dependency constraints.
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48

Ben Belgacem, Mohamed, Nabil Abdennadher, and Marko Niinimaki. "Virtual EZ Grid." International Journal of Handheld Computing Research 3, no. 1 (January 2012): 74–85. http://dx.doi.org/10.4018/jhcr.2012010105.

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This paper presents the Virtual EZ Grid project, based on the XtremWeb-CH (XWCH) volunteer computing platform. The goal of the project is to introduce a flexible distributed computing system, with (i) an infrastructure with a non-trivial amount of computing resources from various institutes, (ii) a stable platform that manages these computing resources and provides advanced interfaces for applications, and (iii) a set of applications that take benefit of the platform. This paper concentrates on the application support of the new version of XWCH, and describes how two medical applications, MedGIFT and NeuroWeb, utilise it.
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49

Xu, Yan. "Task Scheduling Algorithm Research in Grid Computing." Applied Mechanics and Materials 380-384 (August 2013): 2841–44. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2841.

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This paper studies effective task scheduling problem in the process of grid computing. Generally, task scheduling in the process of grid computing can be realized in shorter time, which guarantees the efficiency of task scheduling in grid computing. Traditional algorithm can not fully consider the resources load balance in calculating task scheduling in grid computing, resulting in network resources idleness. Finally, it can't reasonably use network resources. In order to avoid the above defects, this paper proposes a task scheduling method in grid computing based on double fitness particle swarm optimization algorithm. In the process of grid computing, channel perception method is applied to forecast the amount of grid computing tasks in the channel so as to provide the basis for task scheduling in grid computing. Realize task scheduling in grid computing by the use of double fitness particle swarm optimization algorithm. Experimental results show that under the condition of larger tasks of grid computing, the performance of task scheduling in grid computing by using the algorithm presented in this paper is superior to the traditional particle swarm optimization algorithm and can get ideal task scheduling result.
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50

Sungkar, Armu, and Tena Kogoya. "A REVIEW OF GRID COMPUTING." Computer Science & IT Research Journal 1, no. 1 (April 18, 2020): 1–6. http://dx.doi.org/10.51594/csitrj.v1i1.128.

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Grid computing is a combination of interconnected resources which can be spread all over the world having higher computing capabilities. The benefit of grid computing includes higher computation and memory capacity because of grid resources spread all over the world. The grid computing is managed by intra-grid scope which refers to the methodologies and the algorithms used for managing the grid network related issues such as task scheduling, resource balancing and security of the network. The advantages of grid computing include access to inaccessible resources, resource utilization and balancing, reliability, and parallel computing and scalability. The limitations of the grid computing include application in limited fields and suitability with applications running in batch mode only based on parallel processing
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