Using MapReduce Framework for Mobile Applications

Using MapReduce Framework for Mobile Applications

Adam Dou (University of California, USA), Vana Kalogeraki (Athens University of Economics and Business, Greece), Dimitrios Gunopulos (University of Athens, Greece), Taneli Mielikainen (Nokia Research Center, USA) and Ville H. Tuulos (Nokia Research Center, USA)
DOI: 10.4018/978-1-61350-144-3.ch009
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Most of today’s smart-phones are geared towards a single user experience, whether it is reading a book, watching a movie, playing a game or listening to music. However, there has been a shift towards providing a more complex and social experience: applications are being developed and deployed to help users connect and share information with each other.These applications allow people to keep track of their friends’ statuses in real time, or to help them navigate around traffic congestion. While exciting, most such applications are currently being developed in an ad-hoc nature, reinventing and duplicating a lot of work to support their distributed operations. In this work, we present our framework, Misco. A platform for developing distributed applications for mobile smart-phones. We also explore some existing solutions, applications and related systems. We then discuss some of the many future research paths and show that solutions like ours are just the beginning.
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Basics Of Mapreduce Framework

Smart-phones have been becoming increasingly powerful and are the fastest growing segment in the mobile devices market (Gartner, 2010). A simple look at the evolution of successive generations of iPhones show that their memory and storage has been doubling every year and their processing power has doubled in two years. In terms of these resources, the progression in smart-phones appears to follow Moore’s law. Network speeds have also been increasing with recent migrations from Third Generation (3G) networks which provide 0.2 ~ 14 Mbps to 4G networks which provide 0.1 ~ 1 Gbps (Moray Rumney, 2008).

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