A Grid Based System for Data Mining Using MapReduce

In this paper, we discuss a Grid data mining system based on the MapReduce paradigm of computing. The MapReduce paradigm emphasizes system automation of fault tolerance and redundancy, while keeping the programming model for the user very simple. MapReduce is built closely on top of a distributed file system, that allows efficient distributed storage of large data sets, and allows computation to be scheduled closely to this data. Many machine learning algorithms can be easily integrated into this environment. We explore the potential of the MapReduce paradigm for general large scale data mining. We offer several modifications to the existing MapReduce scheduling system to bring it from a cluster environment to a campus grid that includes desktop PCs, servers and clusters. We provide an example implementation of a machine learning algorithm (the Probabilistic Neural Network) in MapReduce form. We also discuss a MapReduce simulator that can be used to develop further enhancements to the MapReduce system. We provide simulation results for two new proposed scheduling algorithms, designed to improve MapReduce processing on the grid. These scheduling algorithms provide increased storage efficiency and increased job processing speed, when used in a heterogeneous grid environment. This work will be used in the future to produce a fully functioning implementation of the MapReduce runtime system for a grid environment, that will enable easy, data intensive parallel computing for machine learning, with little to no additional hardware investment.

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