Parallel Efficiency for Graph Search

Cloud computing is offering utility oriented IT services to users worldwide. It enables hosting of applications from consumer, scientific and business domains. However data centers hosting cloud computing applications consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. With the increasingly ubiquitous nature of Social Networks and Social Cloud users are starting to explore new ways to interact with and exploit these developing paradigms. Social Networks are used to reflect real world relationships that allow users to share information and form connections between one another. Cloud services allow individuals and businesses to use software and hardware that are managed by third parties at remote locations. Examples of Cloud services include online file storage, social networking sites, webmail, and online business applications. The Social Graph in the Internet context is a graph that depicts personal relations of internet users. The social graph has been referred to as "the global mapping of everybody and how they're related". As we know that social graph have large amount of data. Accessing useful information from large amount of data is very difficult. So to avoid this problem Map Reduce processing paradigm has used. Map Reduce is a programming model for processing large data sets with a parallel, distributed algorithm on a cluster using parallel computing. Parallel computing is a process that simultaneously uses various computing resources to solve problems which has the advantages of speeding up program execution and saving cost. In this paper we are trying to simulate the concept of Map Reduce and to get good parallel efficiency ratio.