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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Many scientific disciplines are now data and information driven, and new scientific knowledge is often gained by scientists putting together data analysis and knowledge discovery “pipelines”. A related trend is that more and more scientific communities realize the benefits of sharing their data and computational services, and are thus contributing to a distributed data and computational community infrastructure (a.k.a. “the Grid”). However, this infrastructure is only a means to an end and scientists ideally should be bothered little with its existence. The goal is for scientists to focus on development and use of what we call scientific workflows. These are networks of analytical steps that may involve, e.g., database access and querying steps, data analysis and mining steps, and many other steps including computationally intensive jobs on high performance cluster computers. In this paper we describe characteristics of and requirements for scientific workflows as identified in a number of our application projects. We then elaborate on Kepler, a particular scientific workflow system, currently under development across a number of scientific data management projects. We describe some key features of Kepler and its underlying Ptolemyii system, planned extensions, and areas of future research. Kepler is a communitydriven, open source project, and we always welcome related projects and new contributors to join. Scientific experiments and large-scale simulations produce massive amounts of data. Many of these scientific datasets are arrays, and are stored in file formats such as HDF5 and NetCDF. Although scientific data management systems, such as SciDB, are designed to manipulate arrays, there are challenges in integrating these systems into existing analysis workflows. Major barriers include the expensive task of preparing and loading data before querying, and converting the final results to a format that is understood by the existing post-processing and visualization tools. As a consequence, integrating a data management system into an existing scientific data analysis workflow is time-consuming and requires extensive user involvement. In this paper, we present the design of a new scientific data analysis system that efficiently processes queries directly over data stored in the HDF5 file format. This design choice eliminates the tedious and error-prone data loading process, and makes the query results readily available to the next processing steps of the analysis workflow. Our design leverages the increasing main memory capacities found in supercomputers through bitmap indexing and in-memory query execution. In addition, query processing over the HDF5 data format can be effortlessly parallelized to utilize the ample concurrency available in large-scale supercomputers and modern parallel file systems. We evaluate the performance of our system on a large supercomputing system and experiment with both a synthetic dataset and a real cosmology observation dataset. Our system frequently outperforms the relational database system that the cosmology team currently uses, and is more than 10X faster than Hive when processing data in parallel. Overall, by eliminating the data loading step, our query processing system is more effective in supporting in situ scientific analysis workflows. While most scientific workflows systems are based on dataflow, some amount of control-flow modeling is often necessary for engineering fault-tolerant, robust, and adaptive workflows. However, control-flow modeling within dataflow often results in workflow specifications that are hard to comprehend, reuse, and maintain. We describe new modeling constructs to address these issues that provide a structured approach for modeling control-flow within scientific workflows, and discuss their implementation within the Kepler scientific workflow system. Cloud computing is gaining tremendous momentum in both academia and industry. In this context, we define the term "Cloud Workflow" as the specification, execution and provenance tracking of large-scale scientific workflows, as well as the management of data and computing resources to support the execution of large-scale scientific workflows in the Cloud. In this paper, we first analyze the gap between these two complementary technologies, and what it means to bring Clouds and workflows together. Then, we present the key challenges in supporting Cloud workflows, and present our reference framework for scientific workflow management in the Cloud. Last we present our experience in integrating a scientific workflow management system-Swift into the Cloud. We discuss the performance of cluster provisioning within the OpenNebula Cloud platform, the Eucalyptus Cloud platform and Amazon EC2, and we demonstrate the capability and efficiency of the integration using a NASA MODIS image processing workflow and the Montage image mosaic workflow.Note to practitioners. Scientific workflow management plays a very important role for scientific computing and application coordination, while Cloud computing offers scalability and resource on-demand. We devise autonomous methods to integrate scientific workflow management systems with Cloud platforms and also provision resources for large scale workflows, which can facilitate scientists to easily manage their workflows in the Cloud, and take advantage of large scale Cloud resources. There are a few integration options and many challenges in the process, and the experience we gain will help researchers in migrating their workflow management systems and workflow applications into the Cloud. We analyze the major challenges of running scientific workflows on the Cloud.We propose a reference framework to standardize the integration.The implementation experience proves that the framework is feasible and extendible.Cluster-recycling mechanism can improve the resource provisioning efficiency.