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up to 6 months
Goal of this thesis is to evaluate if cloud computing technology is a viable for preprocessing large scientific data sets for visualization.
In DLR’s Institute for Software Technology, an application stack is being developed to visualize scientific data from nearly any domain. Most domains are confronted with extremely large data sets. These sets can range from a few gigabytes to terabytes of data. Visualizing these data sets is not trivial and requires a distributed and parallel approach. The data is processed on a cluster, where each node works only on a subset of data. The results from the cluster nodes are merged and send to the client for visualization.
In the scope of this thesis cloud-based system need to be evaluated and compared to the cluster-based systems used at DLR. Cloud systems come with a number of advantages over classic clusters. Scalability and load balancing are some of them. Nevertheless, they also have some downsides. Their distributed nature may affect network communication speed. Another possible problem may arise from file access. In classical clusters the data is in the same physical location as the cluster nodes. This might not be possible in a cloud system. This might present problems with file access times. The thesis should build on existing cloud computing solutions and scientific work to implement an example post-processing algorithm and compare it to a cluster-based solution. At the end an evaluation should be done to determine if cloud-based systems could replace or complement cluster solutions in the future.
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Institute for Software Technology
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