1. P2P Network for Very Large Virtual Environment Proceedings of the ACM symposium on virtual reality software and technology VRST '06

2. Adaptive Computing and Networking Lab 2 Outline Introduction Overview System Structure Hierarchical LOD Object Structure Data Exchange Methods Simulation Conclusion

3. Adaptive Computing and Networking Lab Introduction A centralized architecture is obviously not a good framework to build a truly scalable Virtual Environment (VE). P2P network overlays are potentially proper architecture.

4. Adaptive Computing and Networking Lab Introduction This paper describes a peer connectivity method for visualizing very vast and complex environments and sharing these environments among many users.

5. Adaptive Computing and Networking Lab

6. Key Concept Neighbor peers in a VE are likely to have a lot of data in common For example, if two peers have nearly the same viewpoint in a city model, data stored in both peers will be almost the same.

7. Adaptive Computing and Networking Lab Challenges Finding and maintaining the appropriate peer connectivity is a very difficult problem in a changing environment where peers viewpoint are allowed to move freely and peers can disconnect or appear at any time.

8. Adaptive Computing and Networking Lab

9. Peer Connectivity Method Self-Adaptation: the ability for a requesting peer to dynamically take into account the serving capacity of neighbor peers

10. Adaptive Computing and Networking Lab Peer Connectivity Method Three types of peers ：  Memory Peers (MP) Likened to central servers  Visualization Peers (VP) End-user peers  Connectivity Peers (CP) Assigned the task of achieving the connectivity between peers

11. Adaptive Computing and Networking Lab Peer Connectivity Method Each CP manages all VPs connected to it and, upon a request of a connected VP, it provides a list of the N VP neighbors, N being a parameter of the request. The list of VP neighbors is the allocated serving pool (ASP).

12. Adaptive Computing and Networking Lab

13. Peer Connectivity Method Goal: to distribute the data requests of a VP among its neighbor VPs.

14. Adaptive Computing and Networking Lab Peer Connectivity Method The selection of the serving VPs is made according to the following characterizes:  Time To Server (TTS).  The estimated available data  The upload limit in bandwidth (BPUPL) assigned to this peer for P2P serving tasks.  The number of peers currently served by the serving peer (NBPCS).

15. Adaptive Computing and Networking Lab Simulation Accuracy: ACC = NMISS / NREQ Accuracy at time t is measured as the number of missing nodes (NMISS) in the level-of-detail (LOD) trees with respect to the number of required nodes (NREQ) to render the current viewpoint.

16. Adaptive Computing and Networking Lab Simulation

17. Adaptive Computing and Networking Lab Progressive Building Tree: PBTree

18. Adaptive Computing and Networking Lab LOD Description Tree

19. Adaptive Computing and Networking Lab Using LODDT to render different viewpoints

20. Adaptive Computing and Networking Lab Using LODDT to assess the content of neighbor peer cache

21. Adaptive Computing and Networking Lab Data Exchange Methods

22. Adaptive Computing and Networking Lab Data Exchange Methods Four types data exchange methods ：  From the MP only (C/S)  From the peer having the least TTS  From the closest peer  Using self-adaptive distribution method.

23. Adaptive Computing and Networking Lab Simulation

24. Adaptive Computing and Networking Lab Simulation

25. Adaptive Computing and Networking Lab Simulation

26. Adaptive Computing and Networking Lab Conclusion This paper proposes a new spatially-organized P2P network overlay with intent to obtain self- scalability for a truly scalable Virtual Environment (VE). It takes into account peer proximity along with some parameters and state variables that allow to assess the current serving capacity of the neighbor peers.