Abstract : Cloud computing infrastructures are providing re- sources on demand for tackling the needs of large-scale dis- tributed applications. Determining the amount of resources to allocate for a given computation is a difficult problem though. This paper introduces and compares four automated resource allocation strategies relying on the expertise that can be captured in workflow-based applications. The evaluation of these strategies was carried out on the Aladdin/Grid'5000 testbed using a real ap- plication from the area of medical image analysis. Experimental results show that optimized allocation can help finding a trade- off between amount of resources consumed and applications makespan.