On The Performance of MapReduce: A Stochastic Approach Conference Paper uri icon


  • © 2014 IEEE. MapReduce is a highly acclaimed programming paradigm for large-scale information processing. However, there is no accurate model in the literature that can precisely forecast its run-time and resource usage for a given workload. In this paper, we derive analytic models for shared-memory MapReduce computations, in which the run-time and disk I/O are expressed as functions of the workload properties, hardware configuration, and algorithms used. We then compare these models against trace-driven simulations using our high-performance MapReduce implementation.

author list (cited authors)

  • Ahmed, S. T., & Loguinov, D.

citation count

  • 5

editor list (cited editors)

  • Lin, J. J., Pei, J., Hu, X., Chang, W. o., Nambiar, R., Aggarwal, C. C., ... Pyne, S.

publication date

  • October 2014