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This paper presents an experimental study of application-level Quality of Service (QoS) on the EGEE production grid. Two experiments are reported. First, the design of a Grid Application-Level QoS Service is presented and compared to the DIANE pilot-job framework. Providing dynamic monitoring, the Java Job Submission system is then evaluated in similar conditions. Results show that these systems are able to reach similar QoS than pilot jobs in many cases. However, robustness issues remain and infrastructure monitoring still has to be improved to provide more autonomy to application-level grid job handling systems.
The Canadian Brain Imaging Research Platform (CBRAIN) is a web-based collaborative research platform developed in response to the challenges raised by data-heavy, compute-intensive neuroimaging research. CBRAIN offers transparent access to remote data sources, distributed computing sites, and an array of processing and visualization tools within a controlled, secure environment. Its web interface is accessible through any modern browser and uses graphical interface idioms to reduce the technical expertise required to perform large-scale computational analyses. CBRAIN's flexible meta-scheduling has allowed the incorporation of a wide range of heterogeneous computing sites, currently including nine national research High Performance Computing (HPC) centers in Canada, one in Korea, one in Germany, and several local research servers. CBRAI...
Grids have emerged as a promising technology to handle the data and compute intensive requirements of many application areas. Digital medical image processing is a promising application area for grids. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. The research project AGIR (Grid Analysis of Radiological Data) presented in this paper addresses this challenge through a combined approach: on one hand, leveraging the grid middleware through core grid medical services which target the requirements of medical data processing applications; on the other hand, grid-enabling a panel of applications ranging from algorithmic resea...
This paper presents the architecture of the Virtual Imaging Platform sup- porting the execution of medical image simulation workflows on multiple comput- ing infrastructures. The system relies on the MOTEUR engine for workflow execu- tion and on the DIRAC pilot-job system for workload management. The jGASW code wrapper is extended to describe applications running on multiple infrastruc- tures and a DIRAC cluster agent that can securely involve personal cluster re- sources with no administrator intervention is proposed. Grid data management is complemented with local storage used as a failover in case of file transfer errors. Between November 2010 and April 2011 the platform was used by 10 users to run 484 workflow instances representing 10.8 CPU years. Tests show that a small per- sonal cluster can significantly contribute to a simulat...
DIRAC [DIRAC] [TSA-08] is a software framework for building distributed computing systems. It was primarily designed forthe needs of the LHCb [LHCb] Collaboration, and is now used by many other communities within EGI [EGI] as a primary wayof accessing grid resources. In France, dedicated instances of the service have been deployed in different locations toanswer specific needs. Building upon this existing expertise, France Grilles [FG] initiated last year a project to deploy anational, multi-community instance in order to share expertise and provide a consistent high-quality service. After describingDIRAC main aims and functionalities, this paper presents the motivations for such a project, as well as the wholeorganizational and technical process that led to the establishment of a production instance that already serves 13communities: ...
Grid technologies and infrastructures can contribute to harnessing the full power of computer-aided image analysis into clinical research and practice. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. This chapter reports on the goals, achievements and lessons learned from the AGIR (Grid Analysis of Radiological Data) project. AGIR addresses this challenge through a combined approach. On one hand, leveraging the grid middleware through core grid medical services (data management, responsiveness, compression, and workflows) targets the requirements of medical data processing applications. On the other hand, grid-enabling a...
Grids have emerged as a promising technology to handle the data and compute intensive requirements of many application areas. Digital medical image processing is a promising application area for grids. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. The research project AGIR (Grid Analysis of Radiological Data) presented in this paper addresses this challenge through a combined approach: on one hand, leveraging the grid middleware through core grid medical services which target the requirements of medical data processing applications; on the other hand, grid-enabling a panel of applications ranging from algorithmic resea...
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