MapReduce System over Heterogeneous Mobile Devices

MapReduce is a distributed processing algorithm which breaks up large problem sets into small pieces, such that a large cluster of computers can work on those small pieces in an efficient, timely manner. MapReduce was created and popularized by Google, and is widely used as a means of processing large amounts of textual data for the purpose of indexing it for search later on. This paper examines the feasibility of using smart mobile devices in a MapReduce system by exploring several areas, including quantifying the contribution they make to computation throughput, end-user participation, power consumption, and security. The proposed MapReduce System over Heterogeneous Mobile Devices consists of three key components: a server component that coordinates and aggregates results, a mobile device client for iPhone, and a traditional client for reference and to obtain baseline data. A prototypical research implementation demonstrates that it is indeed feasible to leverage smart mobile devices in heterogeneous MapReduce systems, provided certain conditions are understood and accepted. MapReduce systems could see sizable gains of processing throughput by incorporating as many mobile devices as possible in such a heterogeneous environment. Considering the massive number of such devices available and in active use today, this is a reasonably attainable goal and represents an exciting area of study. This paper introduces relevant background material, discusses related work, describes the proposed system, explains obtained results, and finally, discusses topics for further research in this