1. Nadia Ranaldo - Eugenio Zimeo Department of Engineering University of Sannio – Benevento – Italy Grids@Work 2008 ProActive and GCM User Group Orchestrating Services based on Active Objects and Grid Components

2. N. Ranaldo & E. Zimeo Outline Research Context  Composition-based approaches for Grid applications  Service orchestration and choreography The SAWE Workflow Enactment System Orchestration of ProActive/GCM components  Distributed data flow  Dynamic binding Future directions

3. N. Ranaldo & E. Zimeo Grid Applications: Composition-based approaches Complex scientific and business applications as composition of reusable, independent and cooperating software units in large-scale distributed systems  Heterogeneity, dynamicity, scalability, security, etc. Composition in space  Structural relations and interactions among units  Code re-use  Tightly-coupled systems (closed world, well-defined knowledge)  Favoured by component-based architectures Composition in time  Units ordered with respect to temporal dependences  Efficient scheduling and resource usage  Loosely-coupled systems (open world – incremental knowledge, late binding)  Favoured by service-oriented architectures Exploit the advantages of both the approaches for Grid applications

4. N. Ranaldo & E. Zimeo Composition in time: Orchestration of Services Analysis Hypothesis Related work Propose experiments Define steps Prototype computing systems Perform experiments Data collection Visualization Validation Adjust experiment Refine hypothesis Presentation Dissemination Define problemsExperiments Data analysis Discovery Graphical Workflow Editor Workflow Engine (WE) Experiment processes Grid middleware functionalities scheduling data movement monitoring

5. N. Ranaldo & E. Zimeo Workflow engines for e-science Taverna: -Web services based language: Scufl; -FreeFluo: engine -Graphical viz of workflow Kepler: -Actor,director -MoML -Execution models -Ptolemy II -Web Services Triana: -Components -Task graph -Data/control flow DAGMan: -Computing tasks -DAG Pegasus: -Based on DAGMan -VDL -DAG and many others

6. N. Ranaldo & E. Zimeo Towards Service Choreography: Centralized Orchestration Approach Centralized control and data flow Completely independent services High network overhead A workflow is managed by a central workflow engine Late binding Efficient scheduling and QoS criteria fulfillment performed interacting with resource management services (matchmaker, broker, etc.) and parallel execution frameworks (skeletons, parallel libraries, etc.) Centralized control flow – distributed data flow Dynamic dependencies among services

7. N. Ranaldo & E. Zimeo Towards Service Choreography: Distributed Orchestration Approach P2P network of services for discovery, composition and execution Each activity described from the individual perspective of its participating services Better support to dynamic workflows

8. N. Ranaldo & E. Zimeo Semantic and Autonomic WE (SAWE) Compliant to WfMC specification XPDL, BPEL Configurator  Defines process description Engine  Functional management of the process Manager  Monitors engine, running activites, environment  Decides actions to react to events, environmental changes, etc.

9. N. Ranaldo & E. Zimeo Workflows of ProActive/GCM Components A task is performed by a ProActive/GCM component (typically a composite component), which exports a well defined functionality Grid Component Model (GCM) Based on Fractal Target Grid context Parallel computation, deployment, dynamicity, autonomous behaviuor Lookup of already running components Deployment at run-time

10. N. Ranaldo & E. Zimeo Future Engine B A A (run) A Future B (run) Value B (block) B Early-Start Pattern Task anticipation exploiting asynchronous invocations and futures  Default future update strategy (data flow follows invocation flow) Distributed data flow through futures  The lazy message-based update strategy No interactions among tasks and the engine for data updating

11. N. Ranaldo & E. Zimeo Workflows of ProActive Components: Dynamic Binding Dynamic binding (abstract modelling) of ProActive tasks adopting the ProActive Scheduler

12. N. Ranaldo & E. Zimeo Future Directions Distributed data flow based on the lazy message- based future update strategy Dynamic binding of ProActive/GCM components  QoS description through semantic annotations of components for dynamic binding based on user-defined QoS criteria Monitoring of ProActive/GCM components for autonomic behaviour of workflows

13. Thanks for your attention! For further contact: Nadia Ranaldo ranaldo@unisannio.itranaldo@unisannio.it Eugenio Zimeo zimeo@unisannio.itzimeo@unisannio.it