The Map-Reduce Parallelism Framework for Task Scheduling in Grid Computing

Abstract:

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As a rapid developing infrastructure, the grid can share widely distributed computing, storage, data and human resources. In order to improve the usability and QoS of the grid, the job management in the grid is very important, and becomes one of the key research issues in grid computing. Map-Reduce provide an efficient and easy-to-use framework for parallelizing the global optimization procedure. The simulation results show the usefulness and effectiveness of our task scheduling algorithm.

Info:

Periodical:

Edited by:

Yuhang Yang, Xilong Qu, Yiping Luo and Aimin Yang

Pages:

111-115

DOI:

10.4028/www.scientific.net/AMR.216.111

Citation:

Y. X. Pei and Y. Zhang, "The Map-Reduce Parallelism Framework for Task Scheduling in Grid Computing", Advanced Materials Research, Vol. 216, pp. 111-115, 2011

Online since:

March 2011

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Price:

$35.00

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