GridBatch: Cloud Computing for Large-Scale Data-Intensive Batch Applications
CCGRID '08 Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid
The Eucalyptus Open-Source Cloud-Computing System
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
The openCF: an open source computational framework based on web services technologies
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
An open source web service based platform for heterogeneous clusters
ISPA'06 Proceedings of the 4th international conference on Parallel and Distributed Processing and Applications
Lightweight web services for high performace computing
ECSA'07 Proceedings of the First European conference on Software Architecture
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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.