@article {119, title = {Developing scientific workflows from heterogeneous services}, journal = {SIGMOD Rec.}, volume = {35}, year = {2006}, month = {June}, pages = {22{\textendash}28}, abstract = {Scientific Workflows (SWFs) need to utilize components and applications in order to satisfy the requirements of specific workflow tasks. Technology trends in software development signify a move from component-based to service-oriented approach, therefore SWF will inevitably need appropriate tools to discover and integrate heterogeneous services. In this paper we present the SODIUM platform consisting of a set of languages and tools as well as related middleware, for the development and execution of scientific workflows composed of heterogeneous services.}, keywords = {JOpera, scientific workflow management}, issn = {0163-5808}, doi = {10.1145/1147376.1147380}, author = {Aphrodite Tsalgatidou and Georgios Athanasopoulos and Michael Pantazoglou and Cesare Pautasso and Thomas Heinis and Roy Gr{\o}nmo and Hoff Hj{\o}rdis and Arne-J{\o}rgen Berre and Magne Glittum and Simela Topouzidou} } @conference {jopera:2005:icac, title = {Design and Evaluation of an Autonomic Workflow Engine}, booktitle = {2nd International Conference on Autonomic Computing (ICAC-05)}, year = {2005}, month = {June}, pages = {27 - 38}, publisher = {IEEE}, organization = {IEEE}, address = {Seattle, Washington}, abstract = {In this paper we present the design and evaluate the performance of an autonomic workflow execution engine. Although there exist many distributed workflow engines, in practice, it remains a difficult problem to deploy such systems in an optimal configuration. Furthermore, when facing an unpredictable workload with high variability, manual reconfiguration is not an option. Thanks to its autonomic controller, the engine features self-configuration, self-tuning and self-healing properties. The engine runs on a cluster of computers using a tuple space to coordinate its various components. Its autonomic controller monitors its performance and responds to workload variations by altering the configuration. In case failures occur, the controller can recover the workflow execution state from persistent storage and migrate it to a different node of the cluster. Such interventions are carried out without any human supervision. As part of the results of our performance evaluation, we compare different autonomic control strategies and discuss how they can automatically tune the system}, keywords = {automatic configuration, autonomic computing, JOpera, Web service composition}, doi = {10.1109/ICAC.2005.21}, author = {Thomas Heinis and Cesare Pautasso and Gustavo Alonso} }