A Review: Map Reduce Framework for Cloud Computing

  • Authors

    • Mekala Sandhya
    • Ashish Ladda
    • Dr. Uma N Dulhare
    • . .
    • . .
    2018-09-25
    https://doi.org/10.14419/ijet.v7i4.6.20224
  • Data Mining, Cloud, Map Reduce Framework, HDFS (Hadoop Distributed File System), Parallel Programming, Distributed Databases

  • Abstract

    In this generation of Internet, information and data are growing continuously. Even though various Internet services and applications. The amount of information is increasing rapidly. Hundred billions even trillions of web indexes exist. Such large data brings people a mass of information and more difficulty discovering useful knowledge in these huge amounts of data at the same time. Cloud computing can provide infrastructure for large data. Cloud computing has two significant characteristics of distributed computing i.e. scalability, high availability. The scalability can seamlessly extend to large-scale clusters. Availability says that cloud computing can bear node errors. Node failures will not affect the program to run correctly. Cloud computing with data mining does significant data processing through high-performance machine. Mass data storage and distributed computing provide a new method for mass data mining and become an effective solution to the distributed storage and efficient computing in data mining.

     

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  • How to Cite

    Sandhya, M., Ladda, A., Uma N Dulhare, D., ., ., & ., . (2018). A Review: Map Reduce Framework for Cloud Computing. International Journal of Engineering & Technology, 7(4.6), 13-16. https://doi.org/10.14419/ijet.v7i4.6.20224

    Received date: 2018-09-24

    Accepted date: 2018-09-24

    Published date: 2018-09-25