An Improved MapReduce Model for Computation-Intensive Task
2013 ◽
Vol 756-759
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pp. 1701-1705
Keyword(s):
Data Set
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MapReduce is a widely adopted parallel programming model. The standard MapReduce model is designed for data-intensive processing. However, some machine learning algorithms are computation-intensive and time-consuming tasks which process the same data set repeatedly. In this paper, we proposed an improved MapReduce model for computation-intensive algorithms. The model is constructed from a service combination perspective. In the model, the whole task is divided into lots of subtasks taking account into the algorithms parameters, and the datagram with acknowledgement mechanism is used as the communication channel among cluster workers. We took the multifractal detrended fluctuation analysis algorithm as an example to demonstrate the model.
2020 ◽
2021 ◽
pp. 126118
2016 ◽
Vol 231
(22)
◽
pp. 4139-4149
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2009 ◽
Vol 41
(5)
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pp. 2806-2811
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