In cloud Computing, Map Reduce is a current computing standard for important data processing. But those existing techniques in the process are not providing the better performance because of the un-improved resource allocation. To overcome this issue, and to provide the performance improved resource allocation with the proposed technique called Dynamic MR. In Dynamic MR, we have three major steps they are Dynamic Hadoop Slot Allocation it is used to overcome the slot allocation restraint, Speculative Execution Performance Balancing it is to steadiness the performance in cluster of jobs, Slot Prescheduling it is to advance the data vicinity. Finally it improves the performance of Map Reduce workloads. We improve the performance of a Map Reduce cluster via optimizing the slot utilization primarily from two perspectives. First, we can classify the slots into two types, namely, busy slots (i.e., with running tasks) and idle slots (i.e., no running tasks). Given the total number of map and reduce slots configured by users, one optimization approach (i.e., macro-level optimization) is to improve the slot utilization by maximizing the number of busy slots and reducing the number of idle slots. Second, it is worth noting that not every busy slot can be efficiently utilized. Thus, our optimization approach (i.e., micro-level optimization) is to improve the utilization efficiency of busy slots after the macrolevel optimization. Particularly, we identify two main affecting factors: Speculative tasks based on these, we propose DynamicMR, a dynamic utilization optimization framework for MapReduce, to improve the performance of a shared Hadoop cluster under a fair scheduling between users.