Implementation of Parallelization Contract Mechanism Extension of Map Reduce Framework for the Efficient Execution Time over Geo-Distributed

Abstract-The world is surrounded by technology and Internet with extreme dynamic changes day by day in it.Due to that quintillion bytes of data is created. Source of this data is in the form of petabytes and zettabytes,which is known as Big data.Examples of such data are climate information, trajectory information, transaction records, web site usage data etc.As this data is in the abundant form so that not easy to process and require more time to execute.Hadoop is only scalable that is it can reliably store and process petabytes. Hadoop plays an important role in processing and handling big data It includes MapReduce – offline computing engine, HDFS – Hadoop Distributed file system, HBase – online data access.Map Reduce functions as dividing input files into chunks and processing these in a series of parallelizable steps., mapping and reducing constitute the essential phase