The International Arab Journal of Information Technology (IAJIT)


Implementation of Image Processing System using Handover Technique with Map Reduce Based on Big Data in the Cloud Environment

  Cloud computing is the one of the emerging techniqu es to process the big data. Cloud computing is also, known as  service  on  demand.  Large  set  or  large  volume  of  dat a  is  known  as  big  data.  Processing  big  data  (MRI  im ages  and  DICOM  images)  normally  takes  more  time.  Hard  tasks  such  a s  handling  big  data  can  be  solved  by  using  the  concepts  of  hadoop.  Enhancing  the  hadoop  concept  will  help  the  user  to  process  the  large  set  of  images.  The  Hadoop  Distributed  File  System  (HDFS) and Map Reduce are the two default main func tions which is used to enhance hadoop.  HDFS is a hadoop file storing  system,  which  is  used  for  storing  and  retrieving  th e  data.  Map  Reduce  is  the  combination  of  two  functi ons  namely  maps  and  reduces. Map is the process of splitting the inputs  and reduce is the process of integrating the outpu t of map’s input. Recently,  medical  experts  experienced  problems  like  machine  f ailure  and  fault  tolerance  while  processing  the  result  for  the  scanned  data.  A  unique  optimized  time  scheduling  algorithm,   called  Dynamic  Handover  Reduce  Function  (DHRF)  alg orithm  is  introduced  in  the  reduce  function.  Enhancement  of  h adoop  and  cloud  and  introduction  of  DHRF  helps  to  o vercome  the  processing risks, to get optimized result with less  waiting time and reduction in error percentage of  the output image.   

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