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OASIS Team, INRIA Sophia-Antipolis/I3S CNRS, Univ. Nice Christian Delbé Data Grid Explorer

Large Scale Emulation Mobility in ProActive. OASIS Team, INRIA Sophia-Antipolis/I3S CNRS, Univ. Nice Christian Delbé Data Grid Explorer 15/09/03. Oasis Team. O bjets A ctifs, S émantique, I nternet et S écurité Common Project : INRIA, CNRS-I3S, UNSA Created in June 1999

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OASIS Team, INRIA Sophia-Antipolis/I3S CNRS, Univ. Nice Christian Delbé Data Grid Explorer

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  1. Large Scale Emulation Mobility in ProActive OASIS Team, INRIA Sophia-Antipolis/I3S CNRS, Univ. Nice Christian Delbé Data Grid Explorer 15/09/03

  2. Oasis Team Objets Actifs, Sémantique, Internetet Sécurité Common Project : INRIA, CNRS-I3S, UNSA Created in June 1999 Directed by Isabelle Attali (isabelle.attali@sophia.inria.fr) Methods and tools for analysis, construction, validation and maintenance of distributed applications

  3. ProActive Java API+Tools for Parallel, Distributed Computing • Main features : • Remotely accessible Objects (RMI, JINI, UDDI) • Asynchronous Communications with synchronization (automatic futures) • Group Communications, Migration (mobile computations) • XML Deployment Descriptors • Interfaced with various protocols (rsh,ssh,LSF,Globus, ….SOAP) • Visualization and monitoring: IC2D • Requirements : JDK (>= 1.3)

  4. ProActive and the Grid • Suited for the Grid (large and heterogeneous systems, high latency,…) • On going large scale SPMD applications environment : • SPMD API based on group communications • Load balancing based on migration • Fault tolerance • Deployment with XML descriptors

  5. Recent experiment : Jem3D • In cooperation with CAIMAN project (INRIA Sophia) • Solve 3D Maxwell’s equations in electromagnetism 2 main tests : • On a 64 processors cluster • On desktop machines LAN: 252 processors No more available resources...

  6. Objectives with Data Grid Explorer • More resources to confirm scalability • Develop and test new features • new protocols integration • security testing • fault tolerance • ... • Need to validate many models • load balancing • migration discussed later • ...

  7. Migration of Active Objects • Generic mechanism : any active object can migrate • No modification of source code nor bytecode • Weak migration : migration is initiated by the object itself • Automatic and transparent forwarding of: • requests (remote references remain valid) • replies (its previous queries will be fulfilled)

  8. Localization of Active Objects • Two approaches • distributed (forwarders) When it migrates, an object leaves a forwarder which leads to its new location • centralized (location server) When it migrates, an object informs a location server of its new location

  9. S : Source A : Agent F : Forwarder reference Host A S A Host B Host C Host D Localization using forwarders

  10. S : Source A : Agent F : Forwarder reference Host A S Request forwarding forwarding F A F A Host B Host C Host D Localization using forwarders Migration Migration

  11. S : Source A : Agent F : Forwarder reference Host A S F F Host B Host C Host D Localization using forwarders Update location A

  12. S : Source A : Agent F : Forwarder reference Host A S F Host B Host C Host D Localization using forwarders F A Next communications with the new reference

  13. Localization of Active Objects • Two approaches • distributed (forwarders) When it migrates, an object leaves a forwarder which leads to its new location • centralized (location server) When it migrates, an object informs a location server of its new location

  14. Host A Server S A Host B Host C Host D Localization using server S : Source A : Agent reference

  15. Host A Server S Update A Migration Host B Host C Host D Localization using server S : Source A : Agent reference

  16. Host A Server S Update A Migration Host B Host C Host D Localization using server S : Source A : Agent reference

  17. Host A Server S Failed Message A Host B Host C Host D Localization using server S : Source A : Agent reference

  18. Host A Server Ask for new location S Answer Message A Host B Host C Host D Localization using server S : Source A : Agent reference

  19. Hybrid protocol : TTL-TTU Forwarders are better on a MAN … but resources consuming ! Server is better on a LAN … but time consuming ! • Provide an hybrid protocol : • use forwarders for limited period • if chain is broken, use localization server • a • Parameterized by two values : • TTL (Time To Live) : after TTL, forwarder is garbage collected • TTU (Time To Update) : a mobile object update his location every TTU

  20. First Step : validating models • Modeling using Markov chains for predicting response time (Fabrice Huet - 2003) • validate model with simulations and experiments • But hypothesis cannot be fulfilled ! • Infinite number of hosts, homogeneous latency,… • Determine impact of hypothesis variation

  21. Second Step : Determining TTL and TTU • No model of the hybrid protocol (but some insights from previous models) • Determine impact of TTL-TTU values in given conditions • Choose best values for a minimal response time • before deployment • during execution

  22. Conclusion • Our objectives are : • Confirm scalability • Test new features • Validate models, localization TTL-TTU • Our requirements are : • a Java runtime (>=1.3) • ProActive packages

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