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Detecting straggler MapReduce tasks in big data processing infrastructure by neural network

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Abstract

Straggler task detection is one of the main challenges in applying MapReduce for parallelizing and distributing large-scale data processing. It is defined as detecting running tasks on weak nodes. Considering two stages in the Map phase (copy, combine) and three stages of Reduce (shuffle, sort and reduce), the total execution time is the total sum of the execution time of these five stages. Estimating the correct execution time in each stage that results in correct total execution time is the primary purpose of this paper. The proposed method is based on the application of a backpropagation neural network on the Hadoop for the detection of straggler tasks, to estimate the remaining execution time of tasks that is very important in straggler task detection. Results achieved have been compared with popular algorithms in this domain such as LATE, ESAMR and the real remaining time for WordCount and Sort benchmarks, and shown able to detect straggler tasks and estimate execution time accurately. Besides, it supports to accelerate task execution time.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under Grants 61632009 & 61472451, in part by the Guangdong Provincial Natural Science Foundation under Grant 2017A030308006 and High-Level Talents Program of Higher Education in Guangdong Province under Grant 2016ZJ01.

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Correspondence to Guojun Wang.

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Appendix

Appendix

Algorithm A: Calculate progress score or weight for each task based on current phase of each task.

figurea

Algorithm B: Calculate progress score for tasks in map phase.

figureb

Algorithm C: Compute progress score for tasks in reduction phase.

figurec

See Figs. 13, 14 and 15.

Fig. 13
figure13

Relationships of nodes in the executive environment

Fig. 14
figure14

Difference of estimation error for ESAMR and NN in map and reduce tasks for NumberCount benchmark. Points higher than the dashed line represent the high accuracy of our method compare to ESAMR

Fig. 15
figure15

Difference of estimation error for ESAMR and NN in map and reduce tasks for Sort benchmark. Points higher than the dashed line represent the high accuracy of our method compare to ESAMR

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Cite this article

Javadpour, A., Wang, G., Rezaei, S. et al. Detecting straggler MapReduce tasks in big data processing infrastructure by neural network. J Supercomput (2020). https://doi.org/10.1007/s11227-019-03136-6

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Keywords

  • Hadoop
  • Speculative execution
  • Straggler tasks
  • MapReduce
  • Artificial neural network