1. Efficient AOI-Cast for Peer-to-Peer Networked Virtual Environments

2. Adaptive Computing and Networking Laboratory Lab Outline Background Proposed schemes Evaluation Conclusion

3. Adaptive Computing and Networking Laboratory Lab Outline Background Proposed schemes Evaluation Conclusion

4. Adaptive Computing and Networking Laboratory Lab Background Networked Virtual Environment (NVE)  Nodes or Avatars Coordinates Area of Interest (AOI)  Massively Multiplayer Online Game (MMOG) World of Warcraft Second life

5. Adaptive Computing and Networking Laboratory Lab Scalability We would like to have high scalability to support massive users in NVE.  System scalability NVE’s ability to handle a growing number of total users in the system  AOI scalability NVE’s ability to handle a growing number of users within a particular AOI

6. Adaptive Computing and Networking Laboratory Lab System scalability Server-based architecture  Client-Server / Server-Cluster  Problems ： Limited resources All loads are centered on the server  Server-based architecture has low system scalability. Peer-to-Peer (P2P) architecture  Advantages ： Distributing loads to all users Users consume and provide resources  P2P architecture has high system scalability since a user focuses on AOI neighbors.

7. Adaptive Computing and Networking Laboratory Lab AOI scalability How come if there are a large number of nodes in AOI?. Server-based architecture P2P-based architecture

8. Adaptive Computing and Networking Laboratory Lab Goal Bandwidth-Efficient AOI-Cast with  high system scalability and  high AOI scalability for P2P NVEs

9. Adaptive Computing and Networking Laboratory Lab AOI-Cast A node has to send message to all nodes within its AOI. AOI-Cast is a scoped multicast Directly sendingForwarding

10. Adaptive Computing and Networking Laboratory Lab VON – directly sending scheme Direct connection  High consistency  Low latency Too many connections Peak bandwidth consumption exceeds the limitation

11. Adaptive Computing and Networking Laboratory Lab VON – Forwarding model Only connect with enclosing neighbors Pro:  Few connections  Aggregation  Compression Con:  Redundant messages

12. Adaptive Computing and Networking Laboratory Lab APOLO – forwarding scheme Each node connects to closest neighbors in four quadrants (4 out-direction links) Message transmission along the in-direction link No redundant message (spanning tree) Inefficient long (more-hop) message transmission path

13. Adaptive Computing and Networking Laboratory Lab Comparison We focus on reducing the bandwidth consumption, so we design our schemes by forwarding AOI-cast.

14. Adaptive Computing and Networking Laboratory Lab Outline Background Proposed schemes Evaluation Conclusion

15. Adaptive Computing and Networking Laboratory Lab VoroCast & FiboCast We proposed two forwarding AOI-cast schemes to reduce the bandwidth consumption  VoroCast No redundant message Low latency  FiboCast An extension of VoroCast Adjusting the message forwarding frequency by hop-distance dynamically

16. Adaptive Computing and Networking Laboratory Lab VoroCast VoroCast divides the AOI neighbors by Voronoi diagram. Each node has a unique ID and exchanges neighbor list with all neighbors periodically to maintain two-hop-neighbor information.

17. Adaptive Computing and Networking Laboratory Lab VoroCast

18. Adaptive Computing and Networking Laboratory Lab root A B C D E F G H I J K M N O P Q L

19. Adaptive Computing and Networking Laboratory Lab Characteristics Less bandwidth consumption  Aggregation  Compression  Non-redundancy Each node has unique parent Low latency  Without restricting the message forwarding direction (less hops than APOLO)

20. Adaptive Computing and Networking Laboratory Lab FiboCast Users in NVEs may pay more attention to activities that are more obvious in the vicinity. We can adaptively adjust the transmission frequency so that neighbors with more hop counts away receive messages less frequently.

21. Adaptive Computing and Networking Laboratory Lab FiboCast Two variables in a message:  current hop count (cpc): increased each hop  maximal hop count (mcp): set by a Fibonacci sequence with the last being  infinite in a round-robin manner The message is dropped when cpc==mcp E.G.: For a Fibonacci sequence, the maximal hop counts would be 2, 3, 3, 4, 5, 7, 10, , 2, 3, 3, 4, 5, 7, 10, , 1, 2, 3, 3, 4, 5, 7, etc.

22. Adaptive Computing and Networking Laboratory Lab Outline Background Proposed schemes Evaluation Conclusion

23. Adaptive Computing and Networking Laboratory Lab Performance metrics Bandwidth consumption  The major metric to measure the AOI scalability Neighborship consistency  The degree of the knowledge about the AOI neighbors Drift distance  The difference between the virtual position and real position of a node

24. Adaptive Computing and Networking Laboratory Lab Simulation environment 1 sec = 10 steps Map = 1000 x 1000 (unit 2 ) Nodes = 100 ~ 1000 (in increments of 100 nodes ) AOI radius = 200 units Steps = 1000 steps Move speed = 5 units / step by random waypoint pattern Data is compressed by zlib The initial values of Fibonacci number are  F 1 = 0 ； F 2 = 1

25. Adaptive Computing and Networking Laboratory Lab Bandwidth consumption

26. Adaptive Computing and Networking Laboratory Lab Neighborship consistency

27. Adaptive Computing and Networking Laboratory Lab Drift distance

28. Adaptive Computing and Networking Laboratory Lab Outline Background Proposed schemes Evaluation Conclusion

29. Adaptive Computing and Networking Laboratory Lab Conclusion We proposed VoroCast and FiboCast to improve AOI scalability by reducing the bandwidth consumption. VoroCast  Non-redundant message  Apply aggregation and compression mechanisms  Low latency FiboCast  An extension of VoroCast  The neighbors less hops away get messages more frequently than those more hops away  AOI scalability is even better

30. Adaptive Computing and Networking Laboratory Lab Q & A