Summary
Background: Analysis of recent high-dimensional biological data tends to be computationally intensive
as many common approaches such as resampling or permutation tests require the basic
statistical analysis to be repeated many times. A crucial advantage of these methods
is that they can be easily parallelized due to the computational independence of the
resampling or permutation iterations, which has induced many statistics departments
to establish their own computer clusters. An alternative is to rent computing resources
in the cloud, e.g. at Amazon Web Services.
Objectives: In this article we analyze whether a selection of statistical projects, recently
implemented at our department, can be efficiently realized on these cloud resources.
Moreover, we illustrate an opportunity to combine computer cluster and cloud resources.
Methods: In order to compare the efficiency of computer cluster and cloud implementations
and their respective parallelizations we use microarray analysis procedures and compare
their runtimes on the different platforms.
Results: Amazon Web Services provide various instance types which meet the particular needs
of the different statistical projects we analyzed in this paper. Moreover, the network
capacity is sufficient and the paralleli -zation is comparable in efficiency to standard
computer cluster implementations.
Conclusion: Our results suggest that many statistical projects can be efficiently realized on
cloud resources. It is important to mention, however, that workflows can change substantially
as a result of a shift from computer cluster to cloud computing.
Keywords
Biostatistics - cloud computing - computing methodologies - microarray analysis -
statistical computing
