MySRB and SRB - components of a Data Grid

Rajasekar, Arcot; Wan, Michael; Moore, Reagan

doi:10.1109/hpdc.2002.1029930

Cited by 71 publications

(43 citation statements)

References 2 publications

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“…Storage Resource Broker: The Storage Resource Broker (SRB), developed at the San Diego Supercomputing Center, provides access to distributed data from any single point of access [25][26][27][28].…”

Section: Data Gridmentioning

confidence: 99%

Collaborative grid infrastructure for molecular simulations: TheeMinerals minigrid as a prototype integrated compute and data grid

Calleja

Bruin

Tucker

et al. 2005

Molecular Simulation

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This paper describes a prototype grid infrastructure, called the "eMinerals minigrid", for molecular simulation scientists. which is based on an integration of shared compute and data resources. We describe the key components, namely the use of Condor pools, Linux/Unix clusters with PBS and IBM's LoadLeveller job handling tools, the use of Globus for security handling, the use of Condor-G tools for wrapping globus job submit commands, Condor's DAGman tool for handling workflow, the Storage Resource Broker for handling data, and the CCLRC dataportal and associated tools for both archiving data with metadata and making data available to other workers.

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Section: Data Gridmentioning

confidence: 99%

Collaborative grid infrastructure for molecular simulations: TheeMinerals minigrid as a prototype integrated compute and data grid

Calleja

Bruin

Tucker

et al. 2005

Molecular Simulation

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“…Its goal is to federate extremely large numbers of file systems on local PCs and, at the same time, to manage the file replication across those systems, thus creating a single global file system. Similar to OODT, the SDSC Storage Resource Broker [15] is a middleware that provides access to large numbers of heterogeneous data sources. Its query services attempt to retrieve files based on logical information rather than file name or location, in much the same way that OODT maintains profile data.…”

Section: 2mentioning

confidence: 99%

GLIDE: A Grid-Based Light-Weight Infrastructure for Data-Intensive Environments

Mattmann

Malek

Beckman

et al. 2005

Advances in Grid Computing - EGC 2005

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Abstract. The promise of the grid is that it will enable public access and sharing of immense amounts of computational and data resources among dynamic coalitions of individuals and institutions. However, the current grid solutions make several limiting assumptions that curtail their widespread adoption in the emerging decentralized, resource constrained, embedded, autonomic, and mobile (DREAM) environments: they are designed primarily for highly complex scientific problems, and therefore require powerful hardware and reliable network connectivity; additionally, they provide no application design support to grid users (e.g., scientists). To address these limitations, we present GLIDE, a prototype light-weight, data-intensive middleware infrastructure that enables access to the robust data and computational power of the grid on DREAM platforms. We illustrate GLIDE on an example file sharing application. We discuss our early experience with GLIDE and present a set of open research questions.

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“…Section 3 discusses issues connected with the annotation of this data with metadata as well as the process of moving from data to information and knowledge. The need for metadata that adequately annotates distributed collections of scientific data has been emphasized by the Data Intensive Computing Environment (DICE) Group at the San Diego Supercomputer Center [12]. Their Storage Resource Broker data management middleware addresses many of the issues raised here.…”

Section: Introductionmentioning

confidence: 99%

Starting the data conversation: informing data services at an academic health sciences library

Read¹,

Surkis²,

Larson³

et al. 2015

J Med Libr Assoc

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This paper previews the imminent flood of scientific data expected from the next generation of experiments, simulations, sensors and satellites. In order to be exploited by search engines and data mining software tools, such experimental data needs to be annotated with relevant metadata giving information as to provenance, content, conditions and so on. The need to automate the process of going from raw data to information to knowledge is briefly discussed. The paper argues the case for creating new types of digital libraries for scientific data with the same sort of management services as conventional digital libraries in addition to other data-specific services. Some likely implications of both the Open Archives Initiative and e-Science data for the future role for university libraries are briefly mentioned. A substantial subset of this e-Science data needs to archived and curated for long-term preservation. Some of the issues involved in the digital preservation of both scientific data and of the programs needed to interpret the data are reviewed. Finally, the implications of this wealth of e-Science data for the Grid middleware infrastructure are highlighted.

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MySRB and SRB - components of a Data Grid

Cited by 71 publications

References 2 publications

Collaborative grid infrastructure for molecular simulations: TheeMinerals minigrid as a prototype integrated compute and data grid

Collaborative grid infrastructure for molecular simulations: TheeMinerals minigrid as a prototype integrated compute and data grid

GLIDE: A Grid-Based Light-Weight Infrastructure for Data-Intensive Environments

Starting the data conversation: informing data services at an academic health sciences library

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