1. Scheduling for Very Large Virtual Environments Using Visibility and Priorities Chris Faisstnauer, Dieter Schmalstieg, Werner Purgathofer Vienna University of Technology

2. Chris Faisstnauer 2 / 20 Introduction Distributed Virtual Environments / networked games n Contention limited resources (CPU, rendering pipeline) n Network bandwidth limitations n Degradation of the system’s performance Popular approach: client-server setup n Scene managed by server / replicated by clients n Repeatedly transmit update messages to clients n Timely delivery essential  visual error n Message filtering: visibility culling n Overhead: examine all objects for each client

3. Chris Faisstnauer 3 / 20 Problem Discarding updates of invisible objects n Each client own point of view n Examination of all objects for each client n Assume: n = number of objects = number of clients Filtering techniques do not schedule remaining objects n If they exceed network bandwidth  bottleneck Effort: O(n 2 ) Scalability

4. Chris Faisstnauer 4 / 20 Solution Technique to manage transmission of update messages n Constant overhead: O(k) per connected client n Overall computational cost reduced to linear effort n Prioritized scheduling: Priority Round-Robin algorithm u Employing visibility information (culling) u Activity monitoring: unpredictable behavior Virtual environments / networked-games of any size n Output sensitive  scalability n Server-controlled objects / user-controlled avatars Effort: O(k*n)=O(n) for n connected clients

5. Chris Faisstnauer 5 / 20 Related Work Filtering techniques:propagation on need-to-know basis n Area of Interest (exploit communication locality) u Regular subdivision (NPSNET-IV), proximity (DIVE) u Pre-determined inter-cell occlusion (SPLINE) u View cone (AVIARY), visibility culling (RING) n Explicitly registering interest (NPSNET-IV, AVIARY) n Dead Reckoning (NPSNET, PARADISE, NETEFFECT) Network topology: IP-Multicast + Message filtering

6. Chris Faisstnauer 6 / 20 Background: Priority Round-Robin Scheduling technique that combines advantages of n Round-Robin (output sensitive, starvation free) n Multi-Level Feedback Queue (enforces priorities) Elements compete for resources  accumulate error Priorities based on error metric  Error Per Unit (EPU) Goal: minimize cumulative error No traditional sorting Approximate sorting in multiple levels (FIFO) n Elements assigned to level using EPU n Level priority reflects scheduling frequency

7. Chris Faisstnauer 7 / 20 Priority Round-Robin (2/3) Selected elements: A,C,G - B,D,G - A,E,G - B,F,G Repetition Count i = NoElements i * NoLevels Predicted Error = ErrorPerUnit * Repetition Count i=0 i=1 i=2

8. Chris Faisstnauer 8 / 20 Priority Round-Robin (3/3) Assignment of elements to levels according average EPU n Variable size levels n Dynamic VE  dynamic error distribution Varying traversal rate (level i )

9. Chris Faisstnauer 9 / 20 Optimum Traversal Rate ne i no. elements in level i av i average EPU of level i tr i traversal rate of level i

10. Chris Faisstnauer 10 / 20 Using Visibility Information (1/2) Occlusion: indoor (rooms,buildings), outdoor(‘fog of war’) Visibility culling carried out with: n ‘Potentially visible sets’ of cells (pre-computed) n Temporal Bounding Volumes, Update Free Regions

11. Chris Faisstnauer 11 / 20 Using Visibility Information (2/2) Traditional: Visibility cullingRR/FIFO-scheduling New approach:PRR-schedulingVisibility culling Repeatedly schedule k elements per client Effort O(k*n)=O(n) for n connected clients Priority: n Visible: object velocity n Invisible: shortest path to visible area (TBV) visible area (TBV)

12. Chris Faisstnauer 12 / 20 Activity Monitoring Scheduling frequency determined by relation of EPU Unpredictable / rapidly changing behavior  EPU invalid Penalty = error caused by change in EPU Benefit = error advantage by using PRR over using RR Switching: Switch to RR performance (ignore priorities) n Damping: Maximum difference between traversal rates MaxDiff = EPU-interval covered / no. levels

13. Chris Faisstnauer 13 / 20 Evaluation Client-server system n Environment generated from triangulated floorplan Server translated objects along randomized paths n Client visualizes scene Transmit subset of position updates  PRR-scheduling Visual error: distance object position on server / client Evaluation of PRR (Priority Round-Robin) n Comparison PRR vs. plain RR n Performance PRR w/o activity monitoring

14. Chris Faisstnauer 14 / 20 Evaluation Testbed

15. Chris Faisstnauer 15 / 20 Example: uniform error distribution Scheduling 1000 out of 10000 simulated cars (10%) Velocities (in units):10000 cars - velocity  [1, 10] Overall error of PRR is 92% lower than RR

16. Chris Faisstnauer 16 / 20 Example: clustered error distribution Scheduling 1000 out of 10000 simulated cars (10%) Velocities (in units): 500 cars- velocity  [9, 10] 200 cars- velocity  [3, 4] 200 cars- velocity  [3, 4] 7500 cars - velocity  [0.1, 0.5] Overall error of PRR is 307% lower than RR

17. Chris Faisstnauer 17 / 20 Example: activity monitoring on Scheduling 1000 out of 10000 simulated cars (10%) Random movement- velocity  [0.1, 10] shuffled every 10 loops

18. Chris Faisstnauer 18 / 20 Example: activity monitoring off Scheduling 1000 out of 10000 simulated cars (10%) Random movement- velocity  [0.1, 10] shuffled every 10 loops

19. Chris Faisstnauer 19 / 20 Conclusions Enhanced Priority Round-Robin algorithm n Handle transmission of update message server  client n Constant effort per connected client n Update frequency (priorities) determined from u Object behavior u Visibility information n Scalable / graceful degradation Substitute Round-Robin (scalable / graceful degradation) n Very Large Distributed Virtual Environments n Networked Games Handle unpredictable behavior (user-controlled avatars)

20. Chris Faisstnauer 20 / 20 Future Work Scheduling of hierarchical human models (avatars) Employing Multiple Levels of Detail (LOD) Construction of extended environment n Large number of rooms, buildings, open landscapes n Large number of avatars (hierarchical human models) Evaluate perceptual error metrics Evaluate motion data from very large online-games (e.g. “Everquest”, “Ultima Online”)