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Aiming at the problems of low computational efficiency and low clustering performance of clustering algorithms for big data, this paper proposed a clustering algorithm of big data based on the improved ABC algorithm and MapReduce . This algorithm combined the grey wolf optimizer algorithm and ABC algorithm, and improved the exploration and exploitation of the ABC algorithm simultaneously, it could help to improve the clustering performance effectively. The algorithm utilized the chaotic map and backward learning as the initial strategy of ABC colony to improve the solution quality of search procedure. It realized the clustering algorithm based on MapReduce programming model, and realized the clustering process for big data by minimizing the quadratic sum of inner class distances . Experimental results demonstrate that the proposed algorithm improves the clustering quality of big data, and speedups the clustering procedure. [ABSTRACT FROM AUTHOR]

With the development of cloud computing application technology and related research, cloud programming model has also a new technological innovation. In college campus network experimental teaching system, cloud platform experiment has become main content for big data analysis. So, by using HDFS struck, building a stable, practical experiment platform to meet the experiment course system becomes a new topic of the experimental study of campus network in colleges and universities. In this paper, we use open source cloud computing platform Hadoop as the basic platform for big data analysis experiment. The basic experimental platform is used for data processing optimization. HDFS provides the underlying application support for distributed computing storage, and realizes communication between NameNode and DataNode. The user file is stored into the node through the data block, so that the read and write requests of the client can be processed timely, and the data block can be created, deleted, replicated and mapped with a unified schedule of NameNode. At the same time, we can target the experiment process according to experiment methods. [ABSTRACT FROM AUTHOR]