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• W2147579361 abstract "Scheduling parallel applications on computational grids is a difficult task. In order to map the parallel application's tasks onto resources in a efficient way, grid schedulers apply scheduling heuristics. The existing scheduling heuristics can be broadly classified in two approaches: i) bin <i xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>-packing</i> <i xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>schedulers,</i> <i xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>and</i> <i xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>ii)</i> <i xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>replication</i> <i xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>schedulers</i> . The first approach requires complete and accurate information about the applications and the grid environment. The second approach does not use any information but, instead, applies the principle of task replication to achieve good performance. Each of these approaches have drawbacks; attaining accurate and complete information about resources and applications is not always possible in a grid environment, while the redundancy of replication schedulers yield an extra consumption of resources. In this work, we investigate the trade-off between these two approaches. We propose scheduling heuristics that use any available information to perform efficient scheduling of <i xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>bag-of-tasks</i> applications, a subclass of parallel applications. Our results show that judicious use of whatever information is available leads to a reduction on resource consumption, without compromising the application's performance." @default.
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• W2147579361 date "2008-09-01" @default.
• W2147579361 modified "2023-10-01" @default.
• W2147579361 title "Scheduling CPU-Intensive Grid Applications Using Partial Information" @default.
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• W2147579361 doi "https://doi.org/10.1109/icpp.2008.40" @default.
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