Science gateways often rely on workflow engines to execute applications on distributed infrastructures. We investigate six software architectures commonly used to integrate workflow engines into science gateways. In tight integration, the workflow engine shares software components with the science gateway. In service invocation, the engine is isolated and invoked through a specific software interface. In task encapsulation, the engine is wrapped as a computing task executed on the infrastructure. In the pool model, the engine is bundled in an agent that connects to a central pool to fetch and execute workflows. In nested workflows, the engine is integrated as a child process of another engine. In workflow conversion, the engine is integrated through workflow language conversion. We describe and evaluate these architectures with metrics for assessment of integration complexity, robustness, extensibility, scalability and functionality. Tight integration and task encapsulation are the easiest to integrate and the most robust. Extensibility is equivalent in most architectures. The pool model is the most scalable one and meta-workflows are only available in nested workflows and workflow conversion. These results provide insights for science gateway architects and developers.

Workflow management has been widely adopted by scientific communities as a valuable tool to carry out complex experiments. It allows for the possibility to perform computations for data analysis and simulations, whereas hiding details of the complex infrastructures underneath. There are many workflow management systems that offer a large variety of generic services to coordinate the execution of workflows. Nowadays, there is a trend to extend the functionality of workflow management systems to cover all possible requirements that may arise from a user community. However, there are multiple scenarios for usage of workflow systems, involving various actors that require different services to be supported by these systems. In this paper we reflect about the usage scenarios of scientific workflow management based on the practical experience of heavy users of workflow technology from communities in three scientific domains: Astrophysics, Heliophysics and Biomedicine. We discuss the requirements regarding services and information to be provided by the workflow management system for each usage profile, and illustrate how these requirements are fulfilled by the tools these communities currently adopt. This paper contributes to the understanding of properties of future workflow management systems that are important to increase their adoption in a large variety of usage scenarios.

Simulation of beam dynamics is an important step needed to aid the design and configuration of particle beam accelerators. A number of methods and software packages have been developed to address modeling in accelerator physics. However, the results of simulations often need to be validated by simultaneous usage of several packages for solving similar problem and comparing the results. In addition, different packages can be used together, each solving a single step of a problem and forming a workflow of processing steps together. In this paper we describe the Virtual Accelerator environment for modeling beam dynamics on distributed computational resources. We present the concepts and the design of the Virtual Accelerator, describe the prototype implementation and show early results.

Grid workflow management systems automate the orchestration of scientific applications with large computational and data processing needs, but lack control over network resources. Consequently, the management system cannot prevent multiple communication intensive applications to compete for network resources, which leads to unpredictable performance. Currently, the lack of control over network resources may prevent certain applications, i.e. applications that need high capacity and Quality of Service, to utilize Grids. Hence, such applications would use dedicated infrastructures. Because the costs to build dedicated infrastructures may far exceed the cost of using existing Grids, the Grid needs to support mechanisms to optimize the interworking between networks and applications. In this paper, we present the architecture and proof of concept to control network resources from Grid workflow management system and to manage network resources from workflow-enabled applications at run-time. Depending on the current network infrastructure capabilities or future advances, applications may employ existing QoS mechanisms or use application-specific ones to provide the desired network service. We believe that our approach leads to performance improvements in communication intensive applications and enables novel Grid applications, which require optimal interworking between networks and applications.

This paper reports on our research into supporting collaborative distributed applications on the Grid. Our case study application, a Virtual Reactor problem solving environment, was built for simulation of industrially important technology of plasma chemical deposition. It incorporates a number of components distributed among different organizations and requires both high-performance and high-throughput computing, as well as interactivity and advanced visualization. The application has been ported to the Grid environment and tested on a highly heterogeneous infrastructure of the RidGrid testbed. We describe the Virtual Reactor architecture and approaches used to integrate distributed components of the application on the Grid, emphasizing the role of the Migrating Desktop Grid portal and tools that bridge the gap between Grid resources and end-user applications. We outline the main features and functionalities of our RidGrid testbed built within a larger European CrossGrid infrastructure, and describe novel solutions and technical approaches that facilitate the usage of Grid resources and optimize load balancing for parallel applications on heterogeneous resources.