Implementation of dynamic handover reduce function algorithm towards optimizing the result in reduce function
Anusha Medavaka, Siripuri Kiran
Cloud computing is among the arising methods to refine the big data. Cloud computing is additionally, called solution as needed. Huge collection or a huge volume of data is called big data. Processing big data (MRI pictures as well as DICOM pictures) typically takes even more time. Tough jobs such as dealing with big data can be fixed by utilizing the principles of Hadoop. Enhancing the Hadoop idea will certainly assist the individual to refine the big collection of photos. The Hadoop Distributed File System (HDFS) and also Map Reduce are both default major features which are utilized to improve Hadoop. HDFS is a Hadoop document storing system, which is made use of for storing as well as recovering the data. Map Reduce is the mix of 2 features particularly map and also decrease. A map is a procedure of splitting the inputs and also lower is the procedure of incorporating the outcome of the map's input. Just recently, clinical specialists experienced issues like device failing and also mistake resistance while processing the outcome for the checked data. A distinct maximized time organizing formula, called Dynamic Handover Minimize Feature (DHRF) formula is presented in the minimize feature. Improvement of Hadoop as well as cloud and also the intro of DHRF aids to get rid of the processing dangers, to obtain maximized result with much less waiting time as well as a decrease at fault percent of the resulting image.
Anusha Medavaka, Siripuri Kiran. Implementation of dynamic handover reduce function algorithm towards optimizing the result in reduce function. International Journal of Academic Research and Development, Volume 4, Issue 4, 2019, Pages 50-56