Performance Study of Parallel Programming on Cloud Computing Environments Using MapReduce
Wen‐Chung Shih, Shian‐Shyong Tseng, Chao‐Tung Yang · 2010
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.