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In the 70s, the transition from batch systems to interactive computing fueled the widespread diffusion of advances in integrated circuit technology. Grids are facing a similar challenge, namely the seamless integration of the grid power into everyday use. One critical component for this integration is responsiveness, the capacity to support on-demand computing and interactivity. A large contributor to responsiveness is the Quality of Service (QoS) for the job execution time. Grid scheduling is involved at two levels in order to provide QoS: the policy level and the implementation level. The main contributions of this paper are as follows. First, we present a detailed analysis of the performance of the EGEE grid with respect to responsiveness. Second, we define and demonstrate a virtualization scheme, which achieves QoS, schedulability ...
Grids are facing the challenge of moving from batch systems to interactive computing. In the 70s, standalone computer systems have met this challenge, and this was the starting point of pervasive computing. Meeting this challenge will allow grids to be the infrastructure for ambient intelligence and ubiquitous computing. This paper shows that EGEE, the largest world grid, does not yet provide the services required for interactive computing, but that it is amenable to this evolution through relatively modest middleware evolution. A case study on medical image analysis exemplifies the particular needs of ultra-short jobs.
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...
The medical community is producing and manipulating a tremendous volume of digital data for which computerized archiving, processing and analysis is needed. Grid infrastructures are promising for dealing with challenges arising in computerized medicine but the manipulation of medical data on such infrastructures faces both the problem of interconnecting medical information systems to Grid middlewares and of preserving patients' privacy in a wide and distributed multi-user system. These constraints are often limiting the use of Grids for manipulating sensitive medical data. This paper describes our design of a medical data management system taking advantage of the advanced gLite data management services, developed in the context of the EGEE project, to fulfill the stringent needs of the medical community. It ensures medical data protect...
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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