30,403 research outputs found
Complex Grid Computing
This article investigates the performance of grid computing systems whose
interconnections are given by random and scale-free complex network models.
Regular networks, which are common in parallel computing architectures, are
also used as a standard for comparison. The processing load is assigned to the
processing nodes on demand, and the efficiency of the overall computing is
quantified in terms of the respective speed-ups. It is found that random
networks allow higher computing efficiency than their scale-free counterparts
as a consequence of the smaller number of isolated clusters implied by the
former model. At the same time, for fixed cluster sizes, the scale free model
tend to provide slightly better efficiency. Two modifications of the random and
scale free paradigms, where new connections tend to favor more recently added
nodes, are proposed and shown to be more effective for grid computing than the
standard models. A well-defined correlation is observed between the topological
properties of the network and their respective computing efficiency.Comment: 5 pages, 2 figure
Investigating grid computing technologies for use with commercial simulation packages
As simulation experimentation in industry become more computationally demanding, grid computing can be seen as a promising technology that has the potential to bind together the computational resources needed to quickly execute such simulations. To investigate how this might be possible, this paper reviews the grid technologies that can be used together with commercial-off-the-shelf simulation packages (CSPs) used in industry. The paper identifies two specific forms of grid computing (Public Resource Computing and Enterprise-wide Desktop Grid Computing) and the middleware associated with them (BOINC and Condor) as being suitable for grid-enabling existing CSPs. It further proposes three different CSP-grid integration approaches and identifies one of them to be the most appropriate. It is hoped that this research will encourage simulation practitioners to consider grid computing as a technologically viable means of executing CSP-based experiments faster
Using a desktop grid to support simulation modelling
Simulation is characterized by the need to run multiple sets of computationally intensive experiments. We argue that Grid computing can reduce the overall execution time of such experiments by tapping into the typically underutilized network of departmental desktop PCs, collectively known as desktop grids. Commercial-off-the-shelf simulation packages (CSPs) are used in industry to simulate models. To investigate if Grid computing can benefit simulation, this paper introduces our desktop grid, WinGrid, and discusses how this can be used to support the processing needs of CSPs. Results indicate a linear speed up and that Grid computing does indeed hold promise for simulation
Experimental Study of Remote Job Submission and Execution on LRM through Grid Computing Mechanisms
Remote job submission and execution is fundamental requirement of distributed
computing done using Cluster computing. However, Cluster computing limits usage
within a single organization. Grid computing environment can allow use of
resources for remote job execution that are available in other organizations.
This paper discusses concepts of batch-job execution using LRM and using Grid.
The paper discusses two ways of preparing test Grid computing environment that
we use for experimental testing of concepts. This paper presents experimental
testing of remote job submission and execution mechanisms through LRM specific
way and Grid computing ways. Moreover, the paper also discusses various
problems faced while working with Grid computing environment and discusses
their trouble-shootings. The understanding and experimental testing presented
in this paper would become very useful to researchers who are new to the field
of job management in Grid.Comment: Fourth International Conference on Advanced Computing & Communication
Technologies (ACCT), 201
The Locus Algorithm III: A Grid Computing system to generate catalogues of optimised pointings for Differential Photometry
This paper discusses the hardware and software components of the Grid
Computing system used to implement the Locus Algorithm to identify optimum
pointings for differential photometry of 61,662,376 stars and 23,799 quasars.
The scale of the data, together with initial operational assessments demanded a
High Performance Computing (HPC) system to complete the data analysis. Grid
computing was chosen as the HPC solution as the optimum choice available within
this project. The physical and logical structure of the National Grid computing
Infrastructure informed the approach that was taken. That approach was one of
layered separation of the different project components to enable maximum
flexibility and extensibility
Plug in to grid computing
This article discusses the potential benefits of grid computing for future power networks. It is also intended to alert the power system community to the concept of grid computing and to initiate a discussion of its potential applications in future power systems. Much like the Web, the grid can operate over the Internet or any other suitable computer networking technology. Grid computing offers an inexpensive and efficient means for participants to compete (but also cooperate) in providing reliable, cheap, and sustainable electrical energy supply. It also provides a relatively inexpensive new technology allowing the output of embedded generators to be monitored and, when necessary, controlled. Basically, the ability of grid-enabled systems to interact autonomously is vital for small generators where manned operation is likely to be viable
Supporting simulation in industry through the application of grid computing
An increased need for collaborative research, together with continuing advances in communication technology and computer hardware, has facilitated the development of distributed systems that can provide users access to geographically dispersed computing resources that are administered in multiple computer domains. The term grid computing, or grids, is popularly used to refer to such distributed systems. Simulation is characterized by the need to run multiple sets of computationally intensive experiments. Large scale scientific simulations have traditionally been the primary benefactor of grid computing. The application of this technology to simulation in industry has, however, been negligible. This research investigates how grid technology can be effectively exploited by users to model simulations in industry. It introduces our desktop grid, WinGrid, and presents a case study conducted at a leading European investment bank. Results indicate that grid computing does indeed hold promise for simulation in industry
Grid-Computing
"Grid-Computing", ein Mitte der 90er Jahre eingefĂĽhrter Begriff, bezeichnet eine Architektur fĂĽr verteilte Systeme, die auf dem World Wide Web aufbaut und die Web-Vision erweitert. Mit
dem Grid-Computing werden die Ressourcen einer Gemeinschaft, einer sogenannten “virtuellen Organisation” (siehe unten), integriert. Die Hoffnung ist, dass hierdurch rechen- und/oder datenintensiven
Aufgaben, die eine einzelne Organisation nicht lösen kann, handhabbar werden. Ein “Grid” bezeichnet eine nach dem Grid-Computing-Ansatz aufgebaute Rechner-, Netzwerk- und Software-Infrastruktur
zur Teilung von Ressourcen mit dem Ziel, die Aufgaben einer virtuellen Organisation zu erledigen. Zu Beginn war die Möglichkeit, ungenutzte CPU-Ressourcen an anderen Stellen für die eigenen Aufgaben einzusetzen, die wesentlich treibende Kraft für erste Experimente. Internet-Computing-Projekte
wie SETI@Home, distributed.net u.a., bei denen die unbenutzten Rechenzyklen von weltweit verteilten privaten PCs verwendet werden, illustrieren das Potential des Grid-Computing. Die heutigen Grid-Konzepte und die ersten -Prototypen gehen weit über diese Anfänge hinaus. Sie versprechen die transparente Bereitstellung von Diensten unabhängig von der räumlichen Nähe. Es wird erwartet, dass das Grid-Computing die Nutzung von Rechnern und Rechnernetzen so grundlegend verändern wird, wie das Web den Datenaustausch bereits verändert hat
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