The Grid and Cloud User Support Environment (gUSE) enables users convenient and easy access to grid and cloud infrastructures by providing a general purpose, workflow-oriented graphical user interface to create and run workflows on various Distributed Computing Infrastructures (DCIs). Its arrangements for creating and modifying existing workflows are, however, non-intuitive and cumbersome due to the technologies and architecture employed by gUSE. In this paper, we outline the first integrated web-based workflow editor for gUSE with the aim of improving the user experience for those with industrial data workflows and the wider gUSE community. We report initial assessments of the editor's utility based on users' feedback. We argue that combining access to diverse scalable resources with improved workflow creation tools is important for all big data applications and research infrastructures.
While Galaxy is widely used, especially by the biomedical community, the data management capabilities are quite restricted for large data and necessitate data transfers between single jobs of a workflow to the server hosting the back-end of Galaxy. However, Galaxy can map to the highly parallelized enactments of Swift @cite_13 . Another workflow system well established in the biomedical community is Taverna but the workflow canvas is only available as a workbench. The workflows can be shared via the social website myExperiment @cite_14 .
@cite_14 @cite_13
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The Galaxy science portal is a popular gateway to data analysis and computational tools for a broad range of life sciences communities. While Galaxy enables users to overcome the complexities of integrating diverse tools into unified workflows, it has only limited capabilities to execute those tools on the parallel and often distributed high-performance resources that the life sciences fields increasingly requires. We outline here an approach to meet this pressing requirement with the Swift parallel scripting language and its distributed runtime system. Swift's model of computation - implicitly parallel functional dataflow - is an elemental abstraction to which the core computing model of Galaxy maps very closely. We describe an integration between Galaxy and Swift that is transforming Galaxy into a much more powerful science gateway, retaining its user-friendly nature while extending its power to execute highly scalable workflows on diverse parallel environments. 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. 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. Abstract Fast and simple access to computing resources for in-silico experiments and processing of large amounts of data have become crucial factors for researchers in the bioinformatics and biomedical domains. Cloud computing offers the possibility of accessing computing resources for a flexible amount of time and with varying requirements. We discuss how a full cloud stack ranging from Infrastructure-as-a-Service (IaaS) via Platform-as-a-Service (PaaS) to Software-as-a-Service (SaaS) can be built on open source technologies including the integration of high-throughput data transfers in all service types. Based on the scaling capabilities provided by the tools used on the IaaS level we devise a strategy to build a dynamically scaling PaaS offering for building and running workflows using Galaxy. Finally, applications — available as SaaS offerings — for two distinct use cases from the bioinformatics and biomedical domains are presented to demonstrate the feasibility and performance of our solution.