Cloud computing provides a great opportunity for scientists, as it enables large-scale experiments that cannot are too long to run on local desktop machines. Cloud-based computations can be highly parallel, long running and data-intensive, which is desirable for many kinds of scientific experiments. However, to unlock this power, we need a user-friendly interface and an easy-to-use methodology for conducting these experiments. For this reason, we introduce here a formal model of a cloud-based platform and the corresponding open-source implementation. The proposed solution allows to conduct experiments without having a deep technical understanding of cloud-computing, HPC, fault tolerance, or data management in order to leverage the benefits of cloud computing. In the current version, we have focused on biophysics and structural chemistry experiments, based on the analysis of big data from synchrotrons and atomic force microscopy. The domain experts noted the time savings for computing and data management, as well as user-friendly interface.
There are different types of scientific workflow systems such as Kepler @cite_29 , Taverna @cite_14 and Galaxy @cite_16 , which are designed to allow researchers to build their own workflows. The is that our platform provides drop-in components, Smart Connectors, for existing workflow engines: @math researchers can utilise and adapt existing Smart Connectors; @math new types of Smart Connectors would be developed within the framework if necessary. From our best knowledge, there is no other framework having this advantage. SCs are geared toward providing power and flexibility over simplicity.
@cite_29 @cite_14 @cite_16
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Life sciences research is based on individuals, often with diverse skills, assembled into research groups. These groups use their specialist expertise to address scientific problems. The in silico experiments undertaken by these research groups can be represented as workflows involving the co-ordinated use of analysis programs and information repositories that may be globally distributed. With regards to Grid computing, the requirements relate to the sharing of analysis and information resources rather than sharing computational power. The myGrid project has developed the Taverna Workbench for the composition and execution of workflows for the life sciences community. This experience paper describes lessons learnt during the development of Taverna. A common theme is the importance of understanding how workflows fit into the scientists' experimental context. The lessons reflect an evolving understanding of life scientists' requirements on a workflow environment, which is relevant to other areas of data intensive and exploratory science. Copyright © 2005 John Wiley & Sons, Ltd. As next-generation sequencing becomes an indispensible tool for biomedical research, it is crucial to provide analysis solutions that are usable and cost effective for biomedical researchers. Galaxy Cloud addresses this by combining the accessible Galaxy interface with automated management of cloud computing resources. Unlike purpose-built solutions, Galaxy allows users either to use existing tested best practices in the form of workflows or to construct their own analyses for novel tasks. Galaxy Cloud instances are owned and controlled entirely by the user who created them and can be used effectively in secure private clouds. Thus, Galaxy Cloud provides a solution that retains user control and privacy, makes complex analysis accessible and enables the use of practically limitless on-demand computing resources.