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Performance Study of Parallel Programming on Cloud Computing Environments Using MapReduce

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3 Author(s)
Wen-Chung Shih ; Dept. of Inf. Sci. & Applic., Asia Univ., Taichung, Taiwan ; Shian-Shyong Tseng ; Chao-Tung Yang

Divisible load applications have such a rich source of parallelism that their parallelization can significantly reduce their total completion time on cloud computing environments. However, it is a challenge for cloud users, probably scientists and engineers, to develop their applications which can exploit the computing power of the cloud. Using MapReduce, novice cloud programmers can easily develop a high performance cloud application. To examine the performance of programs developed by this approach, we apply this pattern to implement three kinds of applications and conduct experiments on our cloud test-bed. Experimental results show that MapReduce programming is suitable for regular workload applications.

Published in:

Information Science and Applications (ICISA), 2010 International Conference on

Date of Conference:

21-23 April 2010