An Efficient MapReduce Computing Model for Imprecise Applications

Changjian Wang, Yuxing Peng, Mingxing Tang, Dongsheng Li, Shanshan Li and Pengfei You
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Changjian Wang: College of Computer, National University of Defense Technology, Changsha, China
Yuxing Peng: College of Computer, National University of Defense Technology, Changsha, China
Mingxing Tang: College of Computer, National University of Defense Technology, Changsha, China
Dongsheng Li: College of Computer, National University of Defense Technology, Changsha, China
Shanshan Li: College of Computer, National University of Defense Technology, Changsha, China
Pengfei You: College of Computer, National University of Defense Technology, Changsha, China

International Journal of Web Services Research (IJWSR), 2016, vol. 13, issue 3, 46-63

Abstract: Optimizing the Map process is important for the improvement of the MapReduce performance. Many efforts have been devoted into the problem to design more efficient scheduling strategies. However, there exists a kind of MapReduce applications, named imprecise applications, where the imprecise results based on part of map tasks can satisfy the requirements of imprecise applications and thus the job processes can be completed when enough map tasks are processed. According to the feature of imprecise applications, the authors propose an improved MapReduce model, named MapCheckReduce, which can terminate the map process when the requirements of an imprecise application is satisfied. Compared to MapReduce, a Check mechanism and a set of extended programming interfaces are added to MapCheckReduce. The Check mechanism receives and analyzes messages submitted by completed map tasks and then determines whether to terminate the map phase according to the analysis results. The programming interfaces are used by the programmers to define the termination conditions of the map process. A data-prefetching mechanism is designed and implemented in MapCheckReduce which can improve the performance of MapCheckReduce effectively. The MapCheckReduce prototype has been implemented and experiment results verify the feasibility and effectiveness of MapCheckReduce.

Date: 2016
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