University of Newcastle
Cloud computing infrastructures are providing resources on demand for tackling the needs of large-scale distributed 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 application from the area of medical image analysis. Experimental results show that optimized allocation can help finding a tradeoff between amount of resources consumed and applications makespan.