• DocumentCode
    3588728
  • Title

    Model to estimate the size of a Hadoop cluster - HCEm

  • Author

    de Souza Brito, Jose Benedito ; Araujo, Aleteia Patricia F.

  • Author_Institution
    Dept. of Comput. Sci., Univ. de Braslia (UnB), Braslia, Brazil
  • fYear
    2014
  • Firstpage
    859
  • Lastpage
    866
  • Abstract
    This paper describes a model which aims to estimate the size of a cluster running Hadoop framework for the processing of large datasets at a given timeframe. As main contributions it denes (i) a light layer of optimization for MapReduce jobs, (ii) presents a model to estimate the size cluster for a Hadoop framework and (iii) performs tests using a real environment - the Amazon Elastic MapReduce. The proposed approach works with the MapReduce to dene the main configuration parameters and determines computational resources of hosts in the cluster in order to meet the desired runtime for the requirements of a given workload requirement. Thus, the results show that the proposed model is able to avoid to over-allocation or sub-allocation of computing resources on a Hadoop cluster.
  • Keywords
    Big Data; parallel processing; Amazon Elastic MapReduce; Big Data; HCEm; Hadoop cluster size estimation; MapReduce jobs; Complexity theory; Computational modeling; Data models; Memory management; Optimization; Random access memory; Virtualization; Big Data; Clusters; Hadoop; MapReduce; Performance Model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Parallel and Distributed Systems (ICPADS), 2014 20th IEEE International Conference on
  • Type

    conf

  • DOI
    10.1109/PADSW.2014.7097897
  • Filename
    7097897