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Large-Scale Simulation Experimentation and Analysis

Large-Scale Simulation Experimentation and Analysis. Database Programming Using Java. Agenda. Background on problem presented by the simulations. Architecture of the Scalable Data Grid (SDG) solution. Java technology choices. Future Directions for SDG Questions. Simulation Environment.

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Large-Scale Simulation Experimentation and Analysis

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  1. Large-Scale Simulation Experimentation and Analysis Database Programming Using Java Craig E. Ward

  2. Agenda • Background on problem presented by the simulations. • Architecture of the Scalable Data Grid (SDG) solution. • Java technology choices. • Future Directions for SDG • Questions Craig E. Ward

  3. Simulation Environment • Joint Semi-Autonomous Forces (JSAF) environment in Urban Resolve experiments. • Distributed across multiple workstations and two 128-node clusters. • Goal of simulating 1,000,000 entities. Craig E. Ward

  4. Urban Resolve 2015 Sites Craig E. Ward

  5. Logging Simulation Data • Data loggers captured simulation data from JSAF environment • Before analysis, data moved to central store. • Experiments generating multiple terabytes of data. • Data difficult to manage and analyze. Craig E. Ward

  6. Scaleable Data Grid • SDG is a system for distributed collection and analysis of large amounts of data. • Initial prototype deployment for JSAF environment in Urban Resolve 2015 experiment. • Goals • Make the analysis of the data more useful. • Make the management of data more convenient. Craig E. Ward

  7. SDG n-tier Architecture • Java applications • Services between nodes • Services to client simulations • Web application • User Interface • Database engine • MySQL Craig E. Ward

  8. Detection Cube Multidimensional Cubes • Use the raw data to create multidimensional cubes of dimensions of interest. • Dimensions “roll up” into categories of greater abstraction. Craig E. Ward

  9. SDG Cube Implementation Concepts • Cube Description • What the possible ranges are for indices (coordinates) and measures in a Cube. • Cube View • A selection of a subset of the possible indices and measures of interest in a query. • Cube • A instantiation of a Cube that matches a Cube View. • Comprised of Elements with indices and measures. Craig E. Ward

  10. Distributed Communication • Communication between processes and nodes with Remote Method Invocation (RMI). • Cube Manager classes register handles with RMI servers • Managers return handles to Cubes • Descriptions and Views are serialized and passed to clients. Craig E. Ward

  11. Distributed Communications Craig E. Ward

  12. JDBC • Cubes do not exist in their entirety outside of the database tables. • Tables store simple elements of the Cubes. • Measures at the coordinates in relational table. • Elements are very simple Java objects. Craig E. Ward

  13. Cube Generator Processing Craig E. Ward

  14. Threaded Processing • Interface to simulations using sockets and pooled Thread objects. • Internal mechanisms use Java 5 Executor classes and thread-safe queues from the concurrent processing package. • Generator • Query • Aggregation Craig E. Ward

  15. SDG Web Application • HTML • Forms for query selection. • Display of results of queries. • Java Server Pages • Create HTML pages. • JavaScript • Manipulate DOM to display form. • Java servlets • Interface between backend and JSP pages. Craig E. Ward

  16. Java Technologies used • JDBC • Interaction with the database. • RMI • Communication between nodes. • Threads • “Traditional” and new Java 5 concurrent classes. • Sockets • Interface to simulations. Craig E. Ward

  17. Technologies Not Used • Java Data Objects • Complicates build process. • Hibernate • Another product to configure and distribute. • Java EE • Too complex. • Clustered JDBC (Sequoia) • Requires that tables be uniform. Craig E. Ward

  18. Future SDG Development (?) • Enhance web application with JSF, Ajax. • Caching of cube view results. • Develop ad hoc query capability. • Implement an algebra for manipulating cubes. • Dynamic update of cube descriptions. • Dynamic reconfiguration of communication links. Craig E. Ward

  19. Questions & Comments Craig E. Ward

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