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Bill Helfinstine Mark Torpey Deborah Wilbert Wayne Civinskas Lockheed Martin Information Systems

Experiences with Data Distribution Management in Large-Scale Federations. Joint Experimentation and how it takes advantage of DDM and IP Multicasting. Bill Helfinstine Mark Torpey Deborah Wilbert Wayne Civinskas Lockheed Martin Information Systems.

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Bill Helfinstine Mark Torpey Deborah Wilbert Wayne Civinskas Lockheed Martin Information Systems

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  1. Experiences with DataDistribution Managementin Large-Scale Federations Joint Experimentation and how it takes advantage of DDM and IP Multicasting Bill Helfinstine Mark Torpey Deborah Wilbert Wayne Civinskas Lockheed Martin Information Systems

  2. What is Joint Experimentation and what makes it difficult? • Exploration of possible changes in doctrine, organization, training, material, leadership and people to improve joint operations • In order to explore jointness in an interesting way, a large scenario is needed • Large geographic playbox • Large number of vehicles to populate the playbox • In order to support operators in the loop, real-time operation is needed • In order to support large scenarios, large amounts of hardware and therefore space are needed • Simulation distributed around the country to reduce cost

  3. What do we mean by “Large-scale federations” • Large number of objects (> 30,000) • Large number of federates (> 100) • Large number of sites around the country (5 or more) • Simulation synchronized with real-time clock • Therefore, there is a very large amount of data in playwhich must be handled quickly and efficiently

  4. How can DDM help? • DDM provides a means to tell the RTI what the federate is interested in. • The RTI will do filtering on the data before the federate needs to get involved • Provides a means to dynamically control the flow of data to the simulations • The dynamicism of DDM provides a way to adapt to the changing data requirements of each simulation as the scenario progresses • In a joint exercise, DM is often not powerful enough to describe the data requirements fully

  5. Example Geographic interest regions D B C A

  6. RTI implementations of DDM • Distribute all subscription region information around to all federates, and do source filtering • Include publication region information with each update/interaction and do receiver filtering • Snap publication region to a grid and use the grid to do coarse filtering using the network itself • With a gridded implementation of the DDM, IP multicasting can be employed • IP multicast lets some or all of the filtering be done by the network hardware and operating system, to reduce load on the federate’s process. • Just doing coarse filtering breaks the spec

  7. How has DDM helped Joint Experimentation? • In previous JE events, total object count was much higher than a single federate could accept. Event Federate Count Vehicle Count Object Count MaxObjects PerFederate STOW97 300 3600 7000 400 J9901 100 20000 40000 5000 AO00 150 100000 160000 20000

  8. MC02 challenges for DDM use • Millennium Challenge 2002 will be run using RTI-NG, which does perfect filtering on receive, which interacts poorly with our historical use of DDM • We have not come up with a solution yet that gives us the flexibility and scalability we have enjoyed in the past, due to RTI-s and its tuning specifically for scalability and its implementation of inset grids • JSAF’s “Hyperspace” DDM scheme is hard to explain to other federate developers • Some sort of DDM will probably be necessary due to the data requirements (>30,000 objects at >10 sites) • Gateways to DIS or private protocols make DDM work very poorly

  9. Caveats of using DDM • The design of the DDM scheme is fairly scenario-specific • The operational design of the simulation usually does not take DDM into account • Placement of opfor • Centralized data collection • Operational split by service means joint interaction happens over the WAN • Many federates are designed to require all the data • It is not easy to implement DDM support in a federate • IP multicasting is not well supported by quite a bit of networking hardware

  10. Caveats continued • Loggers and wide-range viewers make DDM ineffective • Since they need to receive almost everything, DDM provides no filtering • Clever data collection design can reduce the data an individual logger receives • Exercise controllers really like map displays with everything on them • The HLA 1.3 spec has a number of requirements that we have heretofore ignored, which significantly reduce flexibility and performance

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