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Table below lists the cloud platforms used in scientific applications.
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{table-plus:title=Table 1: Statistics of cloud platforms used in scientific applications (Sample: Astro: 11 papers, Bio: 15 papers, Env: 10 papers, GIS: 11 papers)}
|| || Astronomy || Biology || Environmental || GIS ||
| Amazon | 6 | 9 | 2 | 3 |
| Azure | | 4 | 1 | |
| Google App Engine | | | | 1 |
| FutureGrid | 1 | 1 | 1 | |
| Magellan | 1 | | 1 | |
| GoGrid | | | 2 | |
| Eucalyptus | 1 | | 2 | |
| Nimbus | | | 1 | |
| OpenNebula | | | | 1 |
| IBM Grid | | | 1 | |
{table-plus} |
A lot of scientific computing applications involve parallel computing algorithms. As a framework to support distributed computing on large data sets on clusters, MapReduce is popular in scientific applications, such as in the field of sequencing analysis in Biology/Bioinformatics (Langmead et al. 2009, Gunarathne et al. 2010). Hadoop is a free opensource implementation of MapReduce, and it is commonly used in science areas such as Biology/Bioinformatics and Astronomy (Wiley et al. 2011). Other MapReduce implementations and extensions are also used such as Microsoft Dryad (Qiu et al. 2009, Lu et al. 2010) and Twister (Qiu et al. 2010).
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