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Exploiting resource profiling mechanism; large-scale scientific computing on grids
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DOI：10.1007/s10586-016-0590-9.png)
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Large-scale scientific applications from various scientific domains (e.g., astronomy, physics, pharmaceuticals, chemistry, etc.) usually require substantial amounts of computing resources and storage space. International Grid computing resources can be a viable choice;
supporting these challenging applications so that effectively locating suitable computing resources with minimal allocation overhead can be crucial. However, Grid resource availability is highly unstable and current Grid In;
mation Service (GIS) cannot provide accurate state in;
mation. This can make it very difficult;
users to schedule the jobs on the Grid system and to map tasks on appropriate available resources. In this paper, we present SCOUT system that can periodically profile Grid computing elements based on available number of CPU cores and average response time, and monitor the per;
mance of each CE in the Virtual Organizations (VO). Micro-benchmark experimental results demonstrate that leveraging profiled data by SCOUT can improve the success rate of task executions and reduce the average response time.
Keyword:
Resource profiling
Job scheduling
Grid computing
Scout
Scientific computing
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