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Nadia Ranaldo - Eugenio Zimeo

Nadia Ranaldo - Eugenio Zimeo. Grids@Work 2008 ProActive and GCM User Group Orchestrating Services based on Active Objects and Grid Components. Department of Engineering University of Sannio – Benevento – Italy. Outline. Research Context Composition-based approaches for Grid applications

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Nadia Ranaldo - Eugenio Zimeo

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

  2. 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 N. Ranaldo & E. Zimeo

  3. 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 N. Ranaldo & E. Zimeo

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

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

  6. Centralized control flow – distributed data flow • Dynamic dependencies among services 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.) N. Ranaldo & E. Zimeo

  7. 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 N. Ranaldo & E. Zimeo

  8. 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. N. Ranaldo & E. Zimeo

  9. 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 N. Ranaldo & E. Zimeo

  10. Future Future Future Value Value 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 A A (run) A Engine B B (run) B (block) B • No interactions among tasks and the engine for data updating N. Ranaldo & E. Zimeo

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

  12. 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 N. Ranaldo & E. Zimeo

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

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