Dynamic Processing Slots Scheduling for I/o Intensive Jobs of Hadoop on Pathology Data

The increasing use of computing resource in our daily lives leads to data generation at an astonishing rate. The computing industry is being repeatedly questioned for its ability to accommodate the unpredictable growth rate of data.It has encouraged the development. Hadoop consists of Hadoop Mapreduce and Hadoop Distributed File System (HDFS), is a platform for large scale data and processing. Distributed processing has become common as the number of data has been increasing rapidly worldwide and the scale of processes has become larger, so that Hadoop has attracted many cloud computing enterprises and technology enthusiasts. Hadoop users are expanding under this situation. My studies are to develop the faster of executing jobs Originated by Hadoop and Hive. Our Proposed work is to set dynamic processing slots scheduling for I/O intensive jobs of Hadoop MapReduce focusing on I/O wait during execution of the pathology data efficiently on the Hadoop cluster or clouds. Assigning more tasks to added free slots when CPU resources with the high rate of I/O wait have been detected on each active Task Tracker node leads to the improvement of 30% of CPU performance.