MapReduce is one of the most popular programming model for big data analysis in Distributed and Parallel Computing Environment. It is used for implementing parallel applications. With the growing development of mobile Internet and cloud computing, the issues related to big data have been a matter of concern in both industry and academy. There are several platforms for users to develop their applications based on MapReduce framework such as Hadoop. Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. This paper discusses various MapReduce applications like Wordcount, Pi, TeraSort, Grep in Cloud based Hadoop. We have shown experimental results of these applications on Amazon EC2 using two types of Ubuntu instances. In this paper, performance of above application has been shown with respect to execution time and number of nodes. We find in our research study that as the number of nodes increases the execution time decreases and performance increases.
A Mobile Ad-Hoc Network (MANET) is a collection of wireless mobile nodes forming a temporary network without using centralized access points, infrastructure, or centralized administration. Routing means the act of moving information across an internet work from a source to a destination. The biggest challenge in this kind of networks is to find a path between the communication end points, what is aggravated through the node mobility. In this paper we present a new routing algorithm for mobile, multi-hop ad-hoc networks. The protocol is based on swarm intelligence. Ant colony algorithms are a subset of swarm intelligence and consider the ability of simple ants to solve complex problems by cooperation. The introduced routing protocol is well adaptive, efficient and scalable. The main goal in the design of the protocol is to reduce the overhead for routing. We refer to the protocol as the Ant Colony Optimization Routing (ACOR).
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