Extending Grids with cloud resource management for scientific computing

From its start using supercomputers, scientific computing constantly evolved to the next levels such as cluster computing, meta-computing, or computational Grids. Today, Cloud Computing is emerging as the paradigm for the next generation of large-scale scientific computing, eliminating the need of hosting expensive computing hardware. Scientists still have their Grid environments in place and can benefit from extending them by leased Cloud resources whenever needed. This paradigm shift opens new problems that need to be analyzed, such as integration of this new resource class into existing environments, applications on the resources and security. The virtualization overheads for deployment and starting of a virtual machine image are new factors which will need to be considered when choosing scheduling mechanisms. In this paper we investigate the usability of compute Clouds to extend a Grid workflow middleware and show on a real implementation that this can speed up executions of scientific workflows.