The INFN-Grid Testbed

Alfieri, Roberto; Barbera, R.; Belluomo, P.; Cavalli, Alessandro; Cecchini, Roberto; Chierici, Andrea; Ciaschini, V.; dell’Agnello, Luca; Donno, Flavia; Ferro, Enrico; Forte, Andrea; Gaido, Luciano; Ghiselli, A.; Gianoli, A.; Italiano, Alessandro; Lusso, S.; Luvisetto, M.L.; Mastroserio, P.; Mazzucato, M.; Mura, D.; Reale, Mario; Salconi, Livio; Sava, G.; Serra, Marco; Spataro, F.; Taurino, F. M.; Tortone, G.; Vaccarossa, Luca; Verlato, M.; Finzi, Giulia Vita

doi:10.1016/j.future.2003.10.002

Cited by 13 publications

(7 citation statements)

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“…The described environment relies on storage services (with replication and catalog services) and computational services provided by the gLite Grid middleware [10]. gLite middleware has been selected for the architecture as the official Grid middleware for the Italian Grid infrastructure and for the EC funded EGEE (Enabling Grids for E-sciencE) project.…”

Section: Methodsmentioning

confidence: 99%

A Grid-based solution for management and analysis of microarrays in distributed experiments

et al. 2007

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Several systems have been presented in the last years in order to manage the complexity of large microarray experiments. Although good results have been achieved, most systems tend to lack in one or more fields. A Grid based approach may provide a shared, standardized and reliable solution for storage and analysis of biological data, in order to maximize the results of experimental efforts. A Grid framework has been therefore adopted due to the necessity of remotely accessing large amounts of distributed data as well as to scale computational performances for terabyte datasets. Two different biological studies have been planned in order to highlight the benefits that can emerge from our Grid based platform. The described environment relies on storage services and computational services provided by the gLite Grid middleware. The Grid environment is also able to exploit the added value of metadata in order to let users better classify and search experiments. A state-of-art Grid portal has been implemented in order to hide the complexity of framework from end users and to make them able to easily access available services and data. The functional architecture of the portal is described. As a first test of the system performances, a gene expression analysis has been performed on a dataset of Affymetrix GeneChip ® Rat Expression Array RAE230A, from the ArrayExpress database. The sequence of analysis includes three steps: (i) group opening and image set uploading, (ii) normalization, and (iii) model based gene expression (based on PM/MM difference model). Two different Linux versions (sequential and parallel) of the dChip software have been developed to implement the analysis and have been tested on a cluster. From results, it emerges that the parallelization of the analysis process and the execution of parallel jobs on distributed computational resources actually improve the performances. Moreover, the Grid environment have been tested both against the possibility of uploading and accessing distributed datasets through the Grid middleware and against its ability in managing the execution of jobs on distributed computational resources. Results from the Grid test will be discussed in a further paper.

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Section: Methodsmentioning

confidence: 99%

A Grid-based solution for management and analysis of microarrays in distributed experiments

et al. 2007

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“…The management of a grid community-a VO-is handled by a VO management service, such as VOMS (Alfieri et al 2005), which aggregates membership information in a form that is accessible to data grid resources.…”

Section: (D ) Security and Virtual Organization Managementmentioning

confidence: 99%

“…In terms of authentication and authorization, the interface between the grid and the local infrastructure is still relatively immature. For the Globus TOOLKIT, the reference approach (grid-mapfile (Alfieri et al 2005)) attributes credentials to the holder of a certificate by mapping them to a local account on the target host.…”

Section: (D ) Security and Virtual Organization Managementmentioning

confidence: 99%

Building a scientific data grid with D i GS

Beckett

Allton

Davies

et al. 2009

Phil. Trans. R. Soc. A.

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We provide an insight into the challenge of building and supporting a scientific data infrastructure with reference to our experience working with scientists from computational particle physics and molecular biology. We illustrate how, with modern high-performance computing resources, even small scientific groups can generate huge volumes (petabytes) of valuable scientific data and explain how grid technology can be used to manage, publish, share and curate these data. We describe the DIGS software application, which we have developed to meet the needs of smaller communities and we have highlighted the key elements of its functionality.

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“…The environment under development is running on the Gilda test bed [3]. We plan an initial setup of two client sites where microarray data may be collected and uploaded to the system.…”

Section: The Proposed Environmentmentioning

confidence: 99%