Preliminary Results on Using Matching Algorithms in Map-Reduce Applications

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Babaii Rizvandi, Nikzad; Taheri, Javid; Zomaya, Albert






In this paper, we study CPU utilization time patterns of several MapReduce applications. After extracting running patterns of several applications, the patterns with their statistical information are saved in a reference database to be later used to tweak system parameters to efficiently execute unknown applications in future. To achieve this goal, CPU utilization patterns of new applications along with its statistical information are compared with the already known ones in the reference database to find/predict their most probable execution patterns. Because of different patterns lengths, the Dynamic Time Warping (DTW) is utilized for such comparison; a statistical analysis is then applied to DTWs’ outcomes to select the most suitable candidates. Moreover, under a hypothesis, another algorithm is proposed to classify applications under similar CPU utilization patterns. Three standard applications (WordCount, Exim Mainlog parsing and Terasort) are used to evaluate our hypothesis in tweaking system parameters in executing similar applications. Results were very promising and showed effectiveness of our approach on pseudo-distributed MapReduce platforms

Mapreduce, Pattern Matching, Configuration parameters, statistical analysis



Babaii Rizvandi, Nikzad; Taheri, Javid; Zomaya, Albert. Preliminary Results on Using Matching Algorithms in Map-Reduce Applications. 2011-03-25. <a href="" target="_blank"></a>

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