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Data Partitioning for Highly Scalable Cloud Applications
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Author(s): Robert Neumann (Otto-von-Guericke-Universität Magdeburg, Germany), Matthias Baumann (Otto-von-Guericke-Universität Magdeburg, Germany), Reiner Dumke (Otto-von-Guericke-Universität Magdeburg, Germany)and Andreas Schmietendorf (Berlin School of Economics and Law, Germany)
Copyright: 2012
Pages: 18
Source title:
Cloud Computing for Teaching and Learning: Strategies for Design and Implementation
Source Author(s)/Editor(s): Lee Chao (University of Houston-Victoria, USA)
DOI: 10.4018/978-1-4666-0957-0.ch016
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Abstract
Cloud computing has brought new challenges, but also exciting chances to developers. With the illusion of an infinite expanse of computing resources, even individual developers have been put into a position from which they can create applications that scale out all over the world, thus affecting millions of people. One difficulty with developing such mega-scale Cloud applications is to keep the storage backend scalable. In this chapter, we detail ways of partitioning non-relational data among thousands of physical storage nodes, thereby emphasizing the peculiarities of tabular Cloud storage. The authors give recommendations of how to establish a sustainable and scaling data management architecture that – while growing in terms of data volume – still provides the same high throughput. Finally, they underline their theoretical elaborations by featuring insights won from a real-world cloud project with which the authors have been involved.
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