Cloud Computing is emerging as a new computing paradigm which aims to provide reliable, customized and QoS guaranteed dynamic computing environments for end users. The availability of these large, virtualized pools of computing resources raises the possibility of a new computing paradigm for scientific research with many advantages. For research groups, Cloud Computing provides convenient access to reliable, high-performance clusters and storage without the need to purchase and maintain sophisticated hardware. For developers, virtualization allows scientific codes to be optimized and pre-installed on machine images, facilitating control over the computational environment. In these large-scale, heterogeneous and dynamic systems, the efficient execution of parallel computations can require mappings of tasks to computing resources whose performance is both irregular (because of heterogeneity) and variable in time (because of dynamicity). This paper introduces our initial experience with Cloud Computing based on a Python implementation of our OpenCF framework. We propose to show the features provided by OpenCF using the Google Application Engine as a proof of concept.
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