Recently MapReduce programming model is becoming popular for large scale data intensive distributed applications due to its efficiency, simplicity and ease of use. The Hadoop implementation of MapReduce is one of the most popular tools for many programmers due its ability to hide details of parallel programming from the users. However work on simulating the Hadoop environment is still in its infancy. Although there are a large number of simulating tools available to simulate distributed environments. However there are only a few simulators available which specifically targets the MapReduce environment. Based on testing we performed; the usability of these simulators is not satisfactory due to the simplified design which limits simulating jobs with variance configurations. We have designed and implemented a MapReduce simulator based on discrete event simulation called MRSim which accurately simulate the Hadoop environment. The simulator on one hand allows us to measure scalability of MapReduce based applications easily and quickly, on the other hand captures the effects of different configurations of Hadoop setup on MapReduce based applications behavior in terms of Hadoop job completion times and hardware utilization.

Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.