Cloud computing is the concept of distributing a work and also processing the same work over the internet. Cloud
computing is called as service on demand. It is always available on the internet in Pay and Use mode. Processing of the Big
Data takes more time to compute MRI and DICOM data. The processing of hard tasks like this can be solved by using the
concept of MapReduce. MapReduce function is a concept of Map and Reduce functions. Map is the process of splitting or
dividing data. Reduce function is the process of integrating the output of the Map’s input to produce the result. The Map
function does two various image processing techniques to process the input data. Java Advanced Imaging (JAI) is introduced
in the map function in this proposed work. The processed intermediate data of the Map function is sent to the Reduce function
for the further process. The Dynamic Handover Reduce Function (DHRF) algorithm is introduced in the reduce function in
this work. This algorithm is implemented in the Reduce function to reduce the waiting time while processing the intermediate
data. The DHRF algorithm gives the final output by processing the Reduce function. The enhanced MapReduce concept and
proposed optimized algorithm is made to work on Euca2ool (a Cloud tool) to produce an effective and better output when
compared with the previous work in the field of Cloud Computing and Big Data.
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