David G. Cameron

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Computational Grids process large, computationally intensive problems on small data sets. In contrast, Data Grids process large computational problems that in turn require evaluating, mining and producing large amounts of data. Replication, creating geographically disparate identical copies of data, is regarded as one of the major optimisation techniques(More)
Computational Grids normally deal with large computation-ally intensive problems on small data sets. In contrast, Data Grids mostly deal with large computational problems that in turn require evaluating and mining large amounts of data. Replication is regarded as one of the major optimisation techniques for providing fast data access. Within this paper,(More)
Grid computing is fast emerging as the solution to the problems posed by the massive computational and data handling requirements of many current international scientific projects. Simulation of the Grid environment is important to evaluate the impact of potential data handling strategies before being deployed on the Grid. In this paper, we look at the(More)
Within the European DataGrid project, Work Package 2 has designed and implemented a set of integrated replica management services for use by data intensive scientific applications. These services, based on the web services model, enable movement and replication of data at high speed from one geographical site to another, management of distributed replicated(More)
Distributed management of data is one of the most important problems facing grids. Within the Enabling Grids for Enabling eScience (EGEE) project, currently the world's largest production grid, a sophisticated hierarchy of data management and storage tools have been developed to help Virtual Organisations (VOs) with this task. In this paper we review the(More)
Optimising the use of Grid resources is critical for users to effectively exploit a Data Grid. Data replication is considered a major technique for reducing data access cost to Grid jobs. This paper evaluates a novel replication strategy , based on an economic model, that optimises both the selection of replicas for running jobs and the dynamic creation of(More)
Many current international scientific projects are based on large scale applications that are both computationally complex and require the management of large amounts of distributed data. Grid computing is fast emerging as the solution to the problems posed by these applications. To evaluate the impact of resource optimisation algorithms, simulation of the(More)
In a worldwide computational Grid, users typically want their jobs to be executed as fast as possible, while the goal of a Grid infrastructure is to assure specific quality of service for all users. In order to reconcile these apparently contrasting goals, the authors proposed an economy-based strategy to be used in a Data Grid for efficient access to and(More)
To provide performant access to data from high energy physics experiments such as the Large Hadron Collider (LHC), controlled replication of files among grid sites is required. Dynamic replication in response to jobs may also be useful, and has been investigated using the grid simulator OptorSim. In this paper, results from simulation of the LHC Computing(More)
The Advanced Resource Connector (ARC) is a lightweight , non-intrusive, simple yet powerful Grid middleware capable of connecting highly heterogeneous computing and storage resources. ARC aims at providing general purpose, flexible, collaborative computing environments suitable for a range of uses, both in science and business. The server side offers the(More)
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