On the performance of MapReduce: A stochastic approach

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.