Implementation of efficient Clouds using MapReduce
Rupali D. Korde, Vilas M. Thakare · 2015
Today, Cloud Computing is having large attention because of provision of configurable computing resources. MapReduce (MR) is one of the popular and widely used frameworks for data intensive distributed computing of batch job. Now a day, Cloud Map Reduce (CMR) is widely used because it is more efficient & run faster than other implementations of the MR framework. Running MapReduce programs in the cloud has big problem of optimization of resource provisioning to reduce the economic cost or job finish time for a specific job, power management and performance. BStream is cloud bursting framework for MapReduce that combine stream processing in the external cloud (EC) with Hadoop in the internal cloud (IC). Stream processing in EC allows pipelined uploading, processing and downloading of data to minimize network latency. To make these systems manageable and scalable, an important research problem is performance. As MapReduce framework works on large datasets which contains some form of information and computation. There are many translators are available to transfer SQL query to MapReduce program but translator for Matlab language is at initial stage. Matlab languag e is essential for mathematical programming .This paper propose the Advance Translator that handles all types of Matlab Commands.