Taking a Look at MapReduce

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With the rapid development of the high-speed internet and information technology, information products and thus promote more widespread use of the digital data is increasingly dramatically. Internet provides convenience to make the people change their habits of the past, by changing the habits and then develop a cloud computing technology, which can be connected in series and use a lot of computers, distributed computing and parallel computing, rapid processing of massive data, so greatly enhance the efficiency of operation processing.
However, cloud computing has gone through several important stages and gradually formed, it is not a new technology but it does inherit a standalone computing, parallel computing, technology and development of distributed computing and grid computing. Leaving the cloud computing gradually becomes a new area.

MapReduce is a distributed computing environment provides a framework for the design of a parallel computing, in order to simplify the development of multi-tasking applications. For parallel processing, the MapReduce design concept is quite suitable for use with particle swarm algorithm with parallel computing concept combined. Therefore, this study will use Hadoop platform, raised MRuPSO algorithm, it was integrated PSO (Particle Swarm Optimization) and MapReduce architecture.

In this study, we focused on the typical Computational Intelligence algorithm: Particle Swarm Optimization (PSO). PSO is a useful utility swarm intelligence
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