Abstract: Hadoop is a software framework for analysis of large data sets. Hadoop distributed file system and map reduce paradigm provide an efficient way to deal with terabyte of data being produced every second. MapReduce is known as a popular way to hold data in the cloud environment due to its excellent scalability and good fault tolerance. However, creating profiles for the same job again and again makes it less efficient. This paper proposes an INTERFACE that optimizes time taken to match sampled mapreduce jobs (Js) with already created profiles. It acts as mediator between profile store and worker (nodes).

• Publication Date: 25-May-2014
• DOI: 10.15224/978-1-63248-028-6-01-16
• Views: 0
• Downloads: 0

Abstract: Cloud computing is an incipient concept combining many fields of computing. Cloud computing provides the services, reducing cost, increasing storage providing flexibility and mobility of information, software and processing capacity over the Internet. However, while achieving the actual realization of these benefits for mobile applications, many new research queries/questions has been opened up. We have surveyed existing work in mobile computing through the prism of cloud computing principle in order to understand how to facilitate the building of mobile cloud-based applications. Though their are many advantages of mobile cloud computing, but their is no open standard available for MCC which handicaps portability of MCC. In this paper we propose a system which overcomes all the challenges in MCC. ‘Open cloud computing’ means any user can access the cloud services provided by any cloud service providers(CSPs).

• Publication Date: 25-May-2014
• DOI: 10.15224/978-1-63248-028-6-01-17
• Views: 0
• Downloads: 0