Author : Neha N. More and V.B. Gadicha

In recent times, Cloud Computing is having large attention due to its necessity for configurable computing resources. MapReduce (MR) is the one of the a popular structure for data-intensive spread computing of batch jobs also for equivalent programming model for cloud computing platform and has develop into the proficient method for dispensation massive data via cluster of computers. Running MapReduce programs in cloud has a big problem of optimization of resource provisioning to decrease the economic cost or job finish time for a specific jobs, power management and performance. As MapReduce framework works on huge datasets which contains various form of information and computation, X-to-MapReduce translator provides a feasible solution to help traditional programmers simply deploy an application on cloud system through translating sequential codes to MapReduce codes. In Recent years, various SQL to-MapReduce translators appear to translate similar like-SQL queries to MapReduce codes also have a superior performance on cloud systems computation. Matlab is an extremely developed language and provides interactive environment for arithmetical computation, visualization, and programming. We propose and develop a simple X-to-MapReduce translator for cloud computing, for basic numerical computations. It translates a Matlab code with up to 100 commands to MapReduce code in only some seconds, which may, In addition, X-to-MapReduce can also identify the dependency among complex commands, which is all the time confusing during hand over coding. We implement and evaluate Matlab commands on a cloud .This paper propose the Advance Translator that handles all types of Matlab Commands.

Keywords: MapReduce, Cloud Computing, Translator, Matlab, Cluster

Article published in International Journal of Current Engineering and Technology, Vol.5, No.2 (April-2015)