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MapReduce is a scheme for processing and managing large scale data sets during a distributed cluster, which has been used for applications such as document clustering, generating search indexes, access log analysis, and numerous other forms of data analytic. In existing system, a hash function is used to partition intermediate data among reduce tasks. During this project the system proposed a decomposition-based distributed algorithm to deal with the large-scale optimization problem for large data application and an online algorithm is additionally designed to adjust data partition and aggregation in a dynamic manner. Network traffic price under both offline and on-line cases is significantly reduced as demonstrated by the extensive stimulation results by the various proposals considered and used. Index Terms -Map phase, Reduce phase, shuffle phase, Aggregator; distributed system;

MapReduce for Big Data Applications Based On Improving the Network Traffic Performance