An experimental study of open-source cloud platforms for dust storm forecasting

Abstract

Cloud computing is becoming a viable computing solution for scientific research and several open-source cloud solutions are available to support scientific studies. However, little has been done to systematically investigate the performance of these solutions in supporting scientific pursuits. Taking dust storm forecasting as an example, we test three popular open-source cloud solutions, namely Eucalyptus, OpenNebula, and CloudStack, on the same hardware and compare against a bare cluster. We find that: (1) compared to the bare cluster, a cloud has about 10% virtualization and management overhead when one virtual machine is used. Overhead increases when more virtual machines are used. Leveraging more virtual resources would not necessarily yield better performance. (2) For computing- and communication-intensive dust storm forecasting, the performance overhead is mainly due to virtualized network rather than virtualized computing resources when more than one virtual machine is involved. (3) Compared to Eucalyptus and CloudStack, OpenNebula provides better support for dust storm forecasting with relatively better performance. The results can provide some insights for scientific community in adopting these open-source cloud solutions.