Improvising block placement policy in HADOOP framework
Bibhudutta Jena, Pradeep Kumar M. Kanaujia, Siddharth Swarup Rautaray, Manjusha Pandey · 2017
Due to the increased market competition increased data management and analysis has landed as in an era that requires further optimization data management and analysis. Big data technologies like apache HADOOP provide a frame work for parallel data processing and generation of analyzed results. MAPREDUCE method is used for analysis of data using various data analysis algorithms like clustering, fragmentation and aggregation. As per the HADOOP architecture the data received from client is distributed to various data node by the name node and in this way a large amount of memory is wasted during the block placement policy, The presented proposal aims to reduce the memory wastage by providing a new technique for block placement policy in Hadoop framework which is also helpful for overcoming the internal fragmentation in HADOOP framework.