Modern scientific applications often need to be distributed across Grids. Increasingly applications rely on services, such as job submission, data transfer or data portal services. We refer to such services as Grid services. While the invocation of Grid services could be hard coded in theory, scientific users want to orchestrate service invocations more flexibly. In enterprise applications, the orchestration of web services is achieved using emerging orchestration standards, most notably the Business Process Execution Language (BPEL). We describe our experience in orchestrating scientific workflows using BPEL. We have gained this experience during an extensive case study that orchestrates Grid services for the automation of a polymorph prediction application. Using this example, we explain the extent with which the BPEL language supports the definition of scientific workflows. We then describe the reliability, performance and scalability that can be achieved by executing a complex scientific workflow with ActiveBPEL, an industrial strength but freely available BPEL engine.

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Service-oriented computing has enabled developers to build large, cross-domain service compositions in a more routine manner. These systems inhabit complex, multi-tier operating environments that pose many challenges to their reliable operation. Unanticipated failures at runtime can be time-consuming to diagnose and may propagate across administrative boundaries. It has been argued that measuring readily available data about system operation can significantly increase the failure management capabilities of such systems. We have built an online monitoring system for cross-domain Web service compositions called Monere, which we use in a controlled experiment involving human operators in order to determine the effects of such an approach on diagnosis times for system-level failures. This paper gives an overview of how Monere is able to instrument relevant components across all layers of a service composition and to exploit the structure of BPEL workflows to obtain structural cross-domain dependency graphs. Our experiments reveal a reduction in diagnosis time of more than 20%. However, further analysis reveals this benefit to be dependent on certain conditions, which leads to insights about promising directions for effective support of failure diagnosis in large Web service compositions. This work is partially supported by the EC's 7 th Framework Programme under grant agreement n 215605 (RESERVOIR) and a BT EPSRC Case studentship.

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