On the performance of MapReduce: A stochastic approach

descriptionPublicationkeyboard_double_arrow_right Article , Conference object 01 Oct 2014Publisher:IEEEJournal:2014 IEEE International Conference on Big Data (Big Data)

Authors: Sarker Tanzir Ahmed; Dmitri Loguinov;

doi: 10.1109/bigdata.2014.7004212

On the performance of MapReduce: A stochastic approach

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Abstract

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.

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