K-Means Method for Grouping in Hybrid MapReduce Cluster

In hybrid cloud computing era, hybrid clusters which are consisted of virtual machines and physical machines become more and more Popular? . MapReduce is a good weapon in this big data era where social computing and multimedia computing are emerging. One of the biggest challenges in hybrid mapreduce cluster is I/O bottleneck which would be aggravated under big data computing. In this paper, we take data locality into consideration and group slave nodes with low intra-communication and high intracommunication. After introducing the architecture and implementation of our grouped hybrid mapreduce cluster (GHMC), we give our k-means algorithm in GHMC and evaluate it with reality environments. The results show that there is a nearly 34.9% performance improvement in our system achieved by the K-means algorithm. Moreover, GHMC system also shows good scalability.