Enhancing the Grid with Cloud Computing

Abstract

Scientific computing has evolved considerably in recent years. Scientific applications have become more complex and require an increasing number of computing resources to perform on a large scale. Grid computing has become widely used and is the chosen infrastructure for many scientific calculations and projects, even though it demands a steep learning curve. The computing and storage resources in the Grid are limited, heterogeneous and often overloaded. This heterogeneity is not only present in the hardware setups, but also in the software composition, where configuration permissions are limited. It also has a negative effect on the portability of scientific applications. The use of Cloud resources could eliminate those constraints. In the Cloud, resources are provisioned on demand and can be scaled up and down, while scientists can easily customize their execution environments in the form of virtual machines. Extending the Grid with Cloud resources would improve the utilization of shared resources and would enable the use of additional resources when the Grid resources are overloaded – known as Cloud bursting. We propose an integration model of the Grid and the Cloud using the HTCondor batch system and the NorduGrid ARC middleware. This model enables batch job execution in any public or private Cloud by deploying a virtualized Grid cluster using the ARC middleware - PaaS model for running Grid applications. An evaluation of the virtual Grid cluster was made and compared with the physical one by running NAMD simulations.