A computinggrid interconnects resources such as high performance computers, scientific databases, and computer-controlled scientific instruments of cooperating organizations each of which is autonomous. It precedes a...
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A computinggrid interconnects resources such as high performance computers, scientific databases, and computer-controlled scientific instruments of cooperating organizations each of which is autonomous. It precedes and is quite different from cloud computing, which provides computing resources by vendors to customers on demand. In this article, we describe the grid computing model and enumerate the major differences between grid and cloud computing.
The massive quantities of geographic information that are collected by modern sensing technologies are difficult to use and understand without data reduction methods that summarize distributions and report salient tre...
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The massive quantities of geographic information that are collected by modern sensing technologies are difficult to use and understand without data reduction methods that summarize distributions and report salient trends. Statistical analyses, therefore, are increasingly, being used to analyze large geographic data sets over a broad spectrum of spatial and temporal scales. Computational grids coordinate the use of distributed computational resources to form a large virtual supercomputer that can be applied to solve computationally intensive problems in science, engineering, and commerce. This paper presents a solution to computing a spatial statistic, G(i)*(d) using grids. Our approach is based on a quadtree-based domain decomposition that uses task-scbeduling algorithms based on gridShell and Condor. Computational experiments carried out on the Teragrid were designed to evaluate the performance of solution processes. The grid-based approach to computing values for G(i)*(d) shows improved performance over the sequential algorithm while also solving larger problem sizes. The solution demonstrated not only advances knowledge about the application of the grid in spatial statistics applications but also provides insights into the design of grid middleware for other computationally intensive applications. Copyright (C) 2008 John Wiley & Sons, Ltd.
Advances in e-Infrastructure promise to revolutionize sensing systems and the way in which data are collected and assimilated, and complex water systems are simulated and visualized. According to the EU Infrastructure...
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Advances in e-Infrastructure promise to revolutionize sensing systems and the way in which data are collected and assimilated, and complex water systems are simulated and visualized. According to the EU Infrastructure 2010 work-programme, data and compute infrastructures and their underlying technologies, either oriented to tackle scientific challenges or complex problem solving in engineering, are expected to converge together into the so-called knowledge infrastructures, leading to a more effective research, education and innovation in the next decade and beyond. grid technology is recognized as a fundamental component of e-Infrastructures. Nevertheless, this emerging paradigm highlights several topics, including data management, algorithm optimization, security, performance (speed, throughput, bandwidth, etc.), and scientific cooperation and collaboration issues that require further examination to fully exploit it and to better inform future research policies. The paper illustrates the results of six different surface and subsurface hydrology applications that have been deployed on the grid. All the applications aim to answer to strong requirements from the Civil Society at large, relatively to natural and anthropogenic risks. grid technology has been successfully tested to improve flood prediction, ground-water resources management and Black Sea hydrological survey, by providing large computing resources. It is also shown that grid technology facilitates e-cooperation among partners by means of services for authentication and authorization, seamless access to distributed data sources, data protection and access right, and standardization. (C) 2011 Elsevier B.V. All rights reserved.
In the mid 1990s, the grid computing community promised the "compute power grid," a utility computing infrastructure for scientists and engineers. Since then, a variety of grids have been built worldwide, fo...
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In the mid 1990s, the grid computing community promised the "compute power grid," a utility computing infrastructure for scientists and engineers. Since then, a variety of grids have been built worldwide, for academic purposes, specific application domains, and general production work. Understanding grid workloads is important for the design and tuning of future grid resource managers and applications, especially in the recent wake of commercial grids and clouds. This article presents an overview of the most important characteristics of grid workloads in the past seven years (2003-2010). Although grid user populations range from tens to hundreds of individuals, a few users dominate each grid's workload both in terms of consumed resources and the number of jobs submitted to the system. Real grid workloads include very few parallel jobs but many independent single-machine jobs (tasks) grouped into single " bags of tasks."
This paper focuses on solving large size combinatorial optimization problems using a grid-enabled framework called ParadisEO-CMW (Parallel and Distributed EO on top on Condor and the Master Worker Framework). The latt...
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This paper focuses on solving large size combinatorial optimization problems using a grid-enabled framework called ParadisEO-CMW (Parallel and Distributed EO on top on Condor and the Master Worker Framework). The latter is an extension of ParadisEO, an open source framework originally intended to the design and deployment of parallel hybrid meta-heuristics on dedicated clusters and networks of workstations. Relying on the Condor-MW framework, it enables the execution of these applications on volatile heterogeneous computational pools of resources. The motivations, architecture and main features will be discussed. The framework has been experimented on a real-world problem: feature selection in near-infrared spectroscopic data mining. It has been solved by deploying a multi-level parallel model of evolutionary algorithms. Experimentations have been carried out on more than 100 PCs originally intended for education. The obtained results are convincing, both in terms of flexibility and easiness at implementation, and in terms of efficiency, quality and robustness of the provided solutions at run time. (C) 2006 Elsevier Inc. All rights reserved.
Developing mathematical optimization models that define multireservoir operating rules that are worthy of being evaluated by simulation models is a challenging task. The traditional approach can easily be scaled, and ...
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Developing mathematical optimization models that define multireservoir operating rules that are worthy of being evaluated by simulation models is a challenging task. The traditional approach can easily be scaled, and grid computing can reduce computation times for large water resource systems. The potential for grid computing in this area of research is largely unexplored. In this paper, the migration of a mixed optimization-simulation approach and preliminary applications to a water system in Southern Italy are presented. Results indicate that grid computing provides significant savings in computation time. (C) 2008 Elsevier Ltd. All rights reserved.
grid computing helps us overcome heterogeneity in terms of computing elements, operating systems, policy decisions, and environments. However, security ❉s impede us from adopting the grid as a widespread IT virtualiza...
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grid computing helps us overcome heterogeneity in terms of computing elements, operating systems, policy decisions, and environments. However, security ❉s impede us from adopting the grid as a widespread IT virtualization solution, so we must develop solutions to address these ❉s.
The demand for computing power in computational electromagnetics (CEM) is continuously increasing. Meanwhile, cooperative engineering is becoming more and more present in daily research and development workflows. Proj...
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The demand for computing power in computational electromagnetics (CEM) is continuously increasing. Meanwhile, cooperative engineering is becoming more and more present in daily research and development workflows. Projects are often developed by teams, which interact remotely, and need tighter and tighter connectivity. grid computing (GC), from the perspective of progress in computer networks, seems a promising way to satisfy both the need of high-performance computing platforms, and the requirements for effective cooperative computing. In this paper, researchers involved in CEM are introduced to grid computing, and to the use of grid computing for CEM. Two real applications are proposed, with a critical discussion on potential benefits and drawbacks with respect to alternative strategies.
Recent activities in grid computing point in the direction of an open service architecture merging computational infrastructure with grid services and Web services. We also see advantages in adding facilities offered ...
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Recent activities in grid computing point in the direction of an open service architecture merging computational infrastructure with grid services and Web services. We also see advantages in adding facilities offered by distributed object computing environments with their interoperable references and services. The use of open service architectures introduces the possibility of composing structures of nested services. In this paper we discuss architectural aspects of middleware for grid computing based on an infrastructure of distributed clusters and/or also services, and an access portal as a demonstration project in progress. Copyright (C) 2004 John Wiley Sons, Ltd.
A catalogue service facilitates sharing, discovery, retrieval, management of, and access to large volumes of distributed geospatial resources, for example data, services, applications, and their replicas on the Intern...
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A catalogue service facilitates sharing, discovery, retrieval, management of, and access to large volumes of distributed geospatial resources, for example data, services, applications, and their replicas on the Internet. grid computing provides an infrastructure for effective use of computing, storage, and other resources available online. The Open Geospatial Consortium has proposed a catalogue service specification and a series of profiles for promoting the interoperability of geospatial resources. By referring to the profile of the catalogue service for Web, an innovative information model of a catalogue service is proposed to offer grid-enabled registry, management, retrieval of and access to geospatial resources and their replicas. This information model extends the e-business registry information model by adopting several geospatial data and service metadata standards the International Organization for Standardization (ISO)'s 19115/19119 standards and the US Federal Geographic Data Committee (FGDC) and US National Aeronautics and Space Administration (NASA) metadata standards for describing and indexing geospatial resources. In order to select the optimal geospatial resources and their replicas managed by the grid, the grid data management service and information service from the Globus Toolkits are closely integrated with the extended catalogue information model. Based on this new model, a catalogue service is implemented first as a Web service. Then, the catalogue service is further developed as a grid service conforming to grid service specifications. The catalogue service can be deployed in both the Web and grid environments and accessed by standard Web services or authorized grid services, respectively. The catalogue service has been implemented at the George Mason University/Center for Spatial Information Science and Systems (GMU/CSISS), managing more than 17 TB of geospatial data and geospatial grid services. This service makes it easy to share and inter
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