Generally, we work in Hadoop with two focal components: Map Reduce and HDFS. In opposition to the flooding capacity of data nodes, the client information is saved by Hadoop based on the group’s room usage of data nodes. Up until now, Hadoop has continued that way since not every datanode is running similar systems. Therefore, if jobs continue to operate on an identical Hadoop group, erratic workload hallmarks will generate poor functionality. Given the Hadoop log documents, I recommend accordingly a dynamic computation to modify the loading on a diverse rack that is being spread through another rack in an identical Hadoop group. Although, given any Hadoop group, transmit any knowledge to the unsecured group with information including assignments that work with delicate or straightforward data, and your privacy will be compromised because of your protected rack. We propose a way to distribute information sharing across different racks while maintaining privacy. Like this, reassigning assignments from the most intensely powered rack to a separate rack enhances the execution of Map Reduce jobs. Our reproductions suggest that the suggestion reduces the operational time of a job on the most heavily powered rack by over 50%.