Million entity distributed simulations
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Million Entity Distributed Simulations. Improving network load by eliminating broadcast interest groups. Presented by: David Prody. State of the Art. In 2005 the Institute for Defense Analysis conducted a million plus entity simulation.

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Million Entity Distributed Simulations

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Million entity distributed simulations

Million Entity Distributed Simulations

Improving network load by eliminating broadcast interest groups

Presented by:

David Prody


State of the art

State of the Art

  • In 2005 the Institute for Defense Analysis conducted a million plus entity simulation.

  • Joint Semi Autonomous Force (JSAF) was used as the driver for entities inside the simulation

  • Scalable Parallel Processor (SPP) where use to provide the horsepower for the simulation

  • These nodes where spread out across the country including several supercomputer sites

    • Maui High Performance Computing Center

    • Aeronautical Systems Center Major Shared Resource Center

    • High Performance Computing Modernization Program (DOD)


How they did it

How they did it

  • Tree of Meshes Routing

    • Groups of nodes used mesh overlays to communicate with other local nodes

    • One node in each local mesh was connected to backbone mesh connecting the 5 primary simulation sites

  • Interest declaration

    • Modified Runtime Infrastructure version s (RTI-s)

      • Simulation nodes specify what data they are interested in.

      • Publish Subscribe model

    • Sender side quelch

      • Original RTI-s used multicast to handle interest groups

      • Smarter routers not only send out data to nodes that have requested it

  • Limited “active entities”

    • Most entities (one million) where civilian “clutter)

    • Only 1000 combat entities total

    • No combat entities used emiiters (no radar or laser)


The problem

The Problem

  • Simulation did not contain enough combat elements to effectively model theater wide combat.

    • More active entities are needed.

    • Modern combat vehicles make extensive use of EMF spectrum.

  • Radar and laser is a major component of a modern battlefield

    • Precision guided weapons

    • Active Radar Tracking/Radar Countermeasures


Problem with emitters

Problem with Emitters

  • Under the current RTI-s interest declaration protocol there is only one emitter interest group

    • If you an interested in any emission you must receive all emissions

    • This is not a scalable solution.


Proposed solution

Proposed Solution

  • Break up emitter interest group into geographic regions.

    • Simulations nodes will be allowed to specify specific geographic regions of interest and only receive emitter traffic from those regions

    • This forces receivers to compute the regions they are able to receive from before they can receive any traffic.

    • This implementation will be done the same as entity position interest groups are handled

      • Battle space is broken in a grid and each grid point is given a unique identifier

      • Receivers publish a list of grid square that they are interested in.


Test bed

Test Bed

  • Computer simulation based on the 2005 large simulation run and personal experience with battlespace simulations

    • 100 nodes in Mesh network

    • Entities distributed evenly throughout nodes

      • This is done for real simulations to prevent computational “hotspots”

    • Only Emitter traffic monitored

    • 4 different distributions of emitter entities “locations” modeled

      • Broadcast – This is the current method. Entity location is irrelevant because controlling node will broadcast to all nodes

      • Random distribution – Entities in each node are randomly assigned a grid location

      • Host Cluster – Entities in the same node have a high probability of being in the same grid.

        • This is closest to how real simulations work

        • Two different distributions where tried. The probability of entities grouping was changed

          • 50% probability – half of the entities on the node where in the same grid

          • 30% probability – 30 % of the entities on the node where in the same grid


Thank you for listening

Thank You for Listening

Any question?

Write: [email protected]


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