Advanced Resource Connector middleware for lightweight computational Grids

Ellert, M.; Grønager, Michael; Константинов, Александр; Kónya, B.; Lindemann, Jonas; Livenson, Ilja; Nielsen, J.; Niinimäki, Marko; Smirnova, O.; Wäänänen, A.

doi:10.1016/j.future.2006.05.008

Cited by 191 publications

(106 citation statements)

References 17 publications

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“…Resource availability in production is about 90%, and middleware e.g. gLite [6] , Globus [7] or ARC [8] cannot handle this without substantial human intervention. Access rights to EGI are primarily organized along the concept of Virtual Organization (VO), and each of the 200 VOs has to be specifically configured on its supporting sites, which adds complexity and introduces extra failures.…”

Section: Motivating Applicationmentioning

confidence: 99%

Efficient distributed monitoring with active Collaborative Prediction

Feng¹,

Germain²,

Glatard³

2013

Future Generation Computer Systems

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Isolating users from the inevitable faults in large distributed systems is critical to Quality of Experience. We formulate the problem of probe selection for fault prediction based on end-to-end probing as a Collaborative Prediction (CP) problem. On an extensive experimental dataset from the EGI grid, the combination of the Maximum Margin Matrix Factorization approach to CP and Active Learning shows excellent performance, reducing the number of probes typically by 80% to 90%. Comparison with other Collaborative Prediction strategies show that Active Probing is most efficient at dealing with the various sources of data variability.

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Section: Motivating Applicationmentioning

confidence: 99%

Efficient distributed monitoring with active Collaborative Prediction

Feng¹,

Germain²,

Glatard³

2013

Future Generation Computer Systems

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“…Scheduler-specific (batch system or grid) interfaces are implemented as plug-ins, loaded at run-time. Presently, plug-ins are implemented for the Grid middleware EGEE (gLite) [10], OSG [11] both direct Condor-G submission or a pilot based job submission system(glideinWMS [12]), and ARC (NorduGrid) [13].In addition plug-ins for batch systems such as LSF and SGE are provided.…”

Section: Crabmentioning

confidence: 99%

Distributed Analysis in CMS

et al. 2010

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CMS expects to manage several Pbytes of data each year, distributing them over many computing sites around the world and enabling data access at those centers for analysis. CMS has identified the distributed sites as the primary location for physics analysis to support a wide community of users, with potentially as many as 3000 users. This represents an unprecedented scale of distributed computing resources and number of users. An overview of the computing architecture, the software tools and the distributed infrastructure deployed is reported. Summaries of the experience in establishing efficient and scalable operations to prepare for CMS distributed analysis are presented, followed by the user experience in their current analysis activities.JournalofGridComputing manuscript No. (will be inserted by the editor) Abstract CMS expects to manage several Pbytes of data each year, distributing them over many computing sites around the world and enabling data access at those centers for analysis. CMS has identified the distributed sites as the primary location for physics analysis to support a wide community of users, with potentially as many as 3000 users. This represents an unprecedented scale of distributed computing resources and number of users. An overview of the computing architecture, the software tools and the distributed infrastructure deployed is reported. Summaries of the experience in establishing efficient and scalable operations to prepare for CMS distributed analysis are presented, followed by the user experience in their current analysis activities. Distributed Analysis in CMS

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“…Large European projects like Enabling Grids for E-science (EGEE) (Jones, 2005) and the European Grid Initiative (EGI) (Laure et al, 2006) have established a continent-wide software and hardware infrastructure for grid applications. Several grid middlewares have been developed in Europe; one of the tasks of the EGI project is to build a unified middleware distribution (EGI_DS, 2008), adopting components from Advanced Resource Connector (ARC) (Ellert et al, 2007), gLite (Laure et al, 2006;Laure & Jones, 2008) and UNICORE (Erwin, 2002). In Switzerland the Swiss Multi Science Computing Grid (SMSCG) project is a connection of many existing resources in a number of organizations (Stockinger et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

A review of medical grids and their direction - A Swiss/Japanese perspective

Nakai¹,

Müller²,

Bagarinao³

et al. 2015

IJRSC

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This paper presents a general and comparative review of the advances of grid technologies in medical sciences since 2000, with a special emphasis on Europe and Japan. The EU has over the years funded several big projects in the Grid and now the cloud area, covering besides the high energy physics domain also bioinformatics and medical applications as in image analysis. Bioinformatics and pharmaceutical design have been the major targets of grid applications in Japan. Grids and clouds share many of the same goals and techniques, although clouds are more commercially oriented and privately provided services and grids are rather publicly initiated resource sharing platforms between institutions without strong economic objectives. In the future, merging these two may potentially solve inter-operation problems of grids, which have been limiting the propagation of biomedical grids.

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Advanced Resource Connector middleware for lightweight computational Grids

Cited by 191 publications

References 17 publications

Efficient distributed monitoring with active Collaborative Prediction

Efficient distributed monitoring with active Collaborative Prediction

Distributed Analysis in CMS

A review of medical grids and their direction - A Swiss/Japanese perspective

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