1. 2001 Fall Simulation Interoperability Workshop Experiences with Data Distribution Management in Large-Scale Federations Bill Helfinstine Mark Torpey Deborah Wilbert Wayne Civinskas Lockheed Martin Information Systems Joint Experimentation and how it takes advantage of DDM and IP Multicasting

2. 2001 Fall Simulation Interoperability Workshop 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. 2001 Fall Simulation Interoperability Workshop 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 play which must be handled quickly and efficiently

4. 2001 Fall Simulation Interoperability Workshop 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. 2001 Fall Simulation Interoperability Workshop Example Geographic interest regions A B D C

6. 2001 Fall Simulation Interoperability Workshop 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. 2001 Fall Simulation Interoperability Workshop How has DDM helped Joint Experimentation? In previous JE events, total object count was much higher than a single federate could accept. 20000160000100000150AO00 50004000020000100J9901 40070003600300STOW97 MaxObjects PerFederate Object Count Vehicle Count Federate Count Event

8. 2001 Fall Simulation Interoperability Workshop 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. 2001 Fall Simulation Interoperability Workshop 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. 2001 Fall Simulation Interoperability Workshop 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