H2Hadoop: Improving Hadoop Performance using the Metadata of Related Jobs

Abstract— Cloud Computing leverages Hadoop framework for processing BigData in parallel. Hadoop has certain limitation s that could be exploited to execute the job efficiently. These limitations are mostly because of data locality in the cluster, jobs and tasks scheduling, and resource allocations in Hadoop. Efficient resource allocation remains a challenge in Cloud Computing MapReduce platforms. We propose H2Hadoop, which is an enhanced Hadoop architecture that reduces the computation cost associated with BigData analysis. The proposed architecture also addresses the issue of resource allocation in native Hadoop. H2Hadoop provides a better solution for “text data” , such as finding DNA sequence and the motif of a DNA sequence. Also, H2Hadoop provides an efficient Data Minin g approach for Cloud Computing environments. H2Hadoop architecture leverages on NameNode’s ability to assign jobs to the TaskTrakers (DataNodes) within the cluster. < final year projects >