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Effective Reduction of Network Traffic Cost in Map Reduce for Very Large Scale Data Applications

K. Reddamma*, D. Jagadeesan**, T. Vivekanandan***

* Research Scholar, Department of Computer Science and Engineering, Sreenivasa Institute of Technology and Management Studies, A.P., India.

** Professor, Department of Computer Science and Engineering, Sreenivasa Institute of Technology and Management Studies, A.P., India.

*** Associate Professor, Department of Computer Science and Engineering, Sreenivasa Institute of Technology and Management Studies, A.P., India.

Periodicity:December - February'2017
DOI : https://doi.org/10.26634/jcom.4.4.13415

Abstract

The MapReduce programming model provides an exciting opportunity to process massive volumes of heterogeneous data using map and reduce tasks in parallel. In the recent time, a number of efforts has been made to improve the performance of the job’s execution. The performance of the job’s execution can be improved further by considering the network traffic. In this paper, an optimistic distributed algorithm is proposed to deal with the significant optimization problem for handling large size data. The optimistic distributed algorithm is more efficient than the distributed algorithm. Finally, simulation results show that the proposal can significantly reduce network traffic cost.

Keywords

Aggregator, Big Data, Hadoop, Hash Table, MapReduce, Optimistic Distributed Algorithm

How to Cite this Article?

Reddamma,K., Jagadeesan,D., and Vivekanandan,T. (2017). Effective Reduction of Network Traffic Cost in Map Reduce for Very Large Scale Data Applications. i-manager’s Journal on Computer Science, 4(4), 14-19. https://doi.org/10.26634/jcom.4.4.13415

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