Abstract
For large data processing in the cloud, MapReduce is a process that can split the data into multiple parts and make a processing slot. The slot based MapReduce is not too effective as it gives the poor performance because of the unoptimized resource allocation and it has the various challenges. The MapReduce job task executions have two unique features. The map slot allocation, only allocates the map task before allocating reduce task to process. The data locality maximization for the efficiency and utilization is required to improve the quality of the system. The modified DynamicMR is a dynamic slot allocation framework to improve the performance of MapReduce. The modified DynamicMR focuses on Hadoop Fair Scheduler (HFS). The Dynamic scheduler consist of three optimization techniques Modified Dynamic Hadoop Slot Allocation (MDHSA), Speculative Execution performance Balancing (SEPB) and Slot Pre-scheduling. Thus the modified DynamicMR provides the effective results compared to the existing system.
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