Performance Improvement of Parallel Programming Model Based on Parameterized Pipelined Map Reduce Approach

Now a day's processing huge amount of data open challenge in web resources. Map Reduce Programming model is solution to this problem. This framework is useful to compute distributed batch of jobs. Output of Mapper and Reducer simplifies fault tolerance problem. We proposed an improved version of the Map Reduce programming model called as Parameterized Pipelined Map Reduce. This model of parameterized pipelined map reduce is used as solution the problems of information recovery. Parameterized pipelined Map Reduce permits data transfer by pipeline with some timing parameter among the processes, growing the batched Map Reduce programming model. Here important thing is obtaining that parameter from mapper, this is done through different policies named as letter based policy, word length policy, sentence based policy, job based policy and analysis based policy. This technique will improve system utilization rate as well as reduce the completion time of the job. In our proposed work we directly send parameter to mapper and reducer. Our result shows 25% performance of system improved.