Abstract
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 area.
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References
Amazon, Inc. Amazon Mechanical Turk, https://www.mturk.com/mturk/welcome
Barroso, L.: Web Search for a Planet: The Google Cluster Architecture. IEEE 23(2) (March 2003)
Blelloch, G.E.: Scans as Primitive Parallel Operations. IEEE Transactions on Computers 38(11) (November 1989)
Dean, J., Ghemawat, J.: Map Reduce, Simplied Data Processing On Large Clusters. ACM, New York (2004)
Dubey, P.: Recognition, Mining, and Synthesis Moves Computers to the Era of Tera. Technology@Intel Magazine (February 2005)
Egha, G.: Worldwide Smartphone Sales Analysis, UK (February 2008)
Folding@Home. Folding@Home project, http://folding.stanford.edu/
Grigorik, I.: Collaborative MapReduce in the Browser (2008)
Hunkins, J.: Will Smartphones be the Next Security Challenge (October 2008)
iPhone Developer Program. iphone development, http://developer.apple.com/iphone/program/develop.html
Krazit, T.: Smartphones Will Soon Turn Computing on its Head, CNet (March 2008)
Ladner, R.E., Fischer, M.J.: Parallel Prex Computation. Journal of the ACM 27(4) (October 1980)
Mitra, S.: Robust System Design with Built-in Soft-Error Resilience. IEEE 38(2) (February 2005)
SETI@Home. SETI@Home Project, http://setiathome.ssl.berkeley.edu/
Zaharia, M., Konwinski, A., Joseph, A.D., Katz, R., Stoica, I.: Improving MapReduce Performance in Heterogeneous Environments. In: OSDI (2008)
Manning, C., Prabhakar, R., Hinrich, S.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Apache Web Server. Apache, http://httpd.apache.org/
Ruby Programming Language Ruby, http://www.ruby-lang.org/en/
PHP Programming Language PHP, http://www.php.net/
jQuery JavaScript Framework. jQuery, http://jquery.com/
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© 2009 Springer-Verlag Berlin Heidelberg
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Elespuru, P.R., Shakya, S., Mishra, S. (2009). MapReduce System over Heterogeneous Mobile Devices. In: Lee, S., Narasimhan, P. (eds) Software Technologies for Embedded and Ubiquitous Systems. SEUS 2009. Lecture Notes in Computer Science, vol 5860. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10265-3_16
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DOI: https://doi.org/10.1007/978-3-642-10265-3_16
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