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1 OASIS Team, INRIA Sophia-Antipolis/I3S CNRS, Univ. Nice Christian Delbé Data Grid Explorer 15/09/03 Large Scale Emulation Mobility in ProActive
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2 Oasis Team Objets Actifs, Sémantique, Internet et 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
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3 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) ProActive
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4 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 ProActive and the Grid
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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...
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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 ...
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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)
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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
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9 S Host A A Host BHost CHost D S : Source A : Agent F : Forwarder reference Localization using forwarders
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10 S Host A Host B A Host CHost D Request forwarding FA F Migration S : Source A : Agent F : Forwarder reference Localization using forwarders
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11 Host B F Host C A Host D Update location F S Host A S : Source A : Agent F : Forwarder reference Localization using forwarders
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12 Host B F Host C A Host D F S Host A Next communications with the new reference S : Source A : Agent F : Forwarder reference Localization using forwarders
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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
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14 S Host A A Host BHost CHost D S : Source A : Agent reference Server Localization using server
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15 S Host A Host B A Host CHost D S : Source A : Agent reference Migration Server Update Localization using server
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16 S Host A Host BHost CHost D S : Source A : Agent reference Migration A Server Update Localization using server
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17 S Host A Host BHost CHost D S : Source A : Agent reference Message A Server Failed Localization using server
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18 S Host A Host BHost CHost D S : Source A : Agent reference A Server Ask for new location Answer Message Localization using server
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19 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 Hybrid protocol : TTL-TTU Forwarders are better on a MAN … but resources consuming ! Server is better on a LAN … but time consuming !
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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
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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
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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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