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Metabolic Flux Analysis in the Cloud

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8 Author(s)
Dalman, T. ; Dept. of Math. & Comput. Sci., Univ. of Marburg, Marburg, Germany ; Dòˆrnemann, T. ; Juhnke, E. ; Weitzel, M.
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The MapReduce pattern popularized by Google has successfully been utilized in several scientific applications. In this paper, it is investigated whether a MapReduce approach utilizing on-demand resources from a Cloud is beneficial to perform simulation tasks in the area of Systems Biology and whether it can be seamlessly integrated into a service-oriented scientific workflow framework. In particular, an Amazon Elastic Map Reduce Cloud implementation of the 13C-MFA (Metabolix Flux Analysis) Monte Carlo bootstrap approach aimed at the integration into an existing BPEL-based scientific workflow system is presented. A comparison of a 64 node MapReduce cluster with a single node computation approach reveals a total performance gain up to a factor of 14, with a total cost for on-demand resources of $11. The most critical factor in terms of performance is I/O, i.e. our application suffers from the fact that I/O operations on many small files are expensive using Amazon S3 and the Hadoop DFS.

Published in:

e-Science (e-Science), 2010 IEEE Sixth International Conference on

Date of Conference:

7-10 Dec. 2010

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