Supporting dynamic parameter sweep in adaptive and user-steered workflow
Proceedings of the 6th workshop on Workflows in support of large-scale science
An adaptive parallel execution strategy for cloud-based scientific workflows
Concurrency and Computation: Practice & Experience
A Provenance-based Adaptive Scheduling Heuristic for Parallel Scientific Workflows in Clouds
Journal of Grid Computing
Dimensioning the virtual cluster for parallel scientific workflows in clouds
Proceedings of the 4th ACM workshop on Scientific cloud computing
Designing a parallel cloud based comparative genomics workflow to improve phylogenetic analyses
Future Generation Computer Systems
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X-ray crystallography is an important field due to its role in drug discovery and its relevance in bioinformatics experiments of comparative genomics, phylogenomics, evolutionary analysis, ortholog detection, and three-dimensional structure determination. Managing these experiments is a challenging task due to the orchestration of legacy tools and the management of several variations of the same experiment. Workflows can model a coherent flow of activities that are managed by scientific workflow management systems (SWfMS). Due to the huge amount of variations of the workflow to be explored (parameters, input data) it is often necessary to execute X-ray crystallography experiments in High Performance Computing (HPC) environments. Cloud computing is well known for its scalable and elastic HPC model. In this paper, we present a performance evaluation for the X-ray crystallography workflow defined by the PC4 (Provenance Challenge series). The workflow was executed using the SciCumulus middleware at the Amazon EC2 cloud environment. SciCumulus is a layer for SWfMS that offers support for the parallel execution of scientific workflows in cloud environments with provenance mechanisms. Our results reinforce the benefits (total execution time 脳 monetary cost) of parallelizing the X-ray crystallography workflow using SciCumulus. The results show a consistent way to execute X-ray crystallography workflows that need HPC using cloud computing. The evaluated workflow shares features of many scientific workflows and can be applied to other experiments.