1. 1 VON: A Scalable Peer-to-Peer Network for Virtual Environments Shun-Yun Hu ( 胡舜元 ) (syhu@yahoo.com) CSIE, National Central University, Taiwan 2005/10/19

2. 2 Outline Introduction Voronoi-based Overlay Network (VON) Simulation Results Analysis Conclusion

3. 3 What is Networked Virtual Environment (NVE)? Virtual Reality + Internet 3D environment with people (avatar), objects, terrain, agents Military simulations (’80)  Massively Multiplayer Online Games (mid-‘90) Trends: larger scale, more realistic simulation

4. 4

5. 5 NVE: A Shared Space

6. 6 Issues for Creating NVE Consistency (events/states) Responsiveness multiplayer Security Scalability Persistency massively multiplayer Reliability (Fault-tolerance)

7. 7 The Scalability Problem Many nodes on a 2D plane ( > 1,000) Message exchange with those within Area of Interest (AOI) How does each node receive the relevant messages? Area of Interest

8. 8 A simple solution (point-to-point) N * (N-1) connections ≈ O(N 2 )  Not scalable! Source: [Funkhouser95]

9. 9 A better solution (client-server) Message filtering at server to reduce traffic N connections = O(N)  server is bottleneck Source: [Funkhouser95]

10. 10 Current solution (server-cluster) Still limited by servers. Expansive to deploy & maintain. Source: [Funkhouser95]

11. 11 Scalability Analysis Scalability constrains Computing resource(CPU) Network resource(Bandwidth) Non-scalable system vs. Scalable system x: number of entities y: resource consumption at the limiting system component Resource limit

12. 12 Server-cluster issues Insufficient total resource Hardware provisioning  over-provision! High user density (crowding) User limits  limits scale & realism! Excessive inter-server communications Less load balancing  difficult balance!

13. 13 What Next? Strategies Increase resource  More servers Decrease consumption  Message filtering ArchitecturesScale Point-to-point (LAN)tens10^1 Client-serverhundreds10^2 Server-clusterthousands10^3 ?millions 10^6 … Peer-to-Peer

14. 14 What is Peer-to-Peer (P2P)? [Stoica et al. 2003] Distributed systems without any centralized control or hierarchical organization Runs software with equivalent functionality Examples File-sharing: Napster, Gnutella, eDonkey Distributed Search: Chord, CAN, Tapestry, Pastry VoIP:Skype

15. 15 Peer-to-Peer Overlay A P2P overlay network source: [Keller & Simon 2003]

16. 16 Promise & Challenge of P2P Promises Growing resource, decentralized  Scalable Commodity hardware  Affordable Challenges Topology maintenance  dynamic join/leave Efficient content retrieval  no global knowledge

17. 17 Issues for Creating P2P NVE Consistency (events/states) Responsiveness multiplayer Security Scalability Persistency massively multiplayer Reliability (Fault-tolerance) Consistency (topology)  P2P NVE

18. 18 Related Work (1): DHT-based: SimMUD [Knutsson et al. 2004] (UPenn) Pastry + Scribe Regions Coordinators (super-nodes) Fixed-size region Relay overhead

19. 19 Related Work (2): Neighbor-list Exchange [Kawahara et al. 2004] (Univ. of Tokyo) Fully-distributed Nearest-neighbors List exchange High transmission Overlay partition

20. 20 Related Work (3): Mutual Notification: Solipsis [Keller & Simon 2003] (France Telecomm R&D) Links with AOI neighbor Mutual cooperation Inside convex hull Potentially slow discovery Inconsistent topology

21. 21 Outline Introduction Voronoi-based Overlay Network (VON) Simulation Results Analysis Conclusion

22. 22 Design Goals Observation: for virtual environment applications, the contents we want are messages from AOI neighbors Content discovery is a neighbor discovery problem Solve the Neighbor Discovery Problem in a fully- distributed, message-efficient manner. Specific goals: Scalable  Limit & minimize message traffics Responsive  Direct connection with AOI neighbors

23. 23 Voronoi Diagram 2D Plane partitioned into regions by sites, each region contains all the points closest to its site Can be used to find k-nearest neighbor easily Neighbors Site Region

24. 24 Design Concepts Identify enclosing and boundary neighbors Each node constructs a Voronoi of its neighbors Enclosing neighbors are minimally maintained Mutual collaboration in neighbor discovery CircleArea of Interest (AOI) Whiteself Yellowenclosing neighbor (E.N.) L. Blueboundary neighbor (B.N.) PinkE.N. & B.N. GreenAOI neighbor L. Greenunknown neighbor Use Voronoi to solve the neighbor discovery problem

25. 25 Procedure (JOIN) 1)Joining node sends coordinates to any existing node Join request is forwarded to acceptor 2)Acceptor sends back its own neighbor list joining node connects with other nodes on the list Acceptor’s region Joining node

26. 26 Procedure (MOVE) 1)Positions sent to all neighbors, mark messages to B.N. B.N. checks for overlaps between mover’s AOI and its E.N. 2)Connect to new nodes upon notification by B.N. Disconnect any non-overlapped neighbor Boundary neighbors New neighbors Non-overlapped neighbors

27. 27 Procedure (LEAVE) 1)Simply disconnect 2)Others then update their Voronoi new B.N. is discovered via existing B.N. Leaving node (also a B.N.) New boundary neighbor

28. 28 Dynamic AOI Crowding within AOI can overload a particular node It’s better if AOI-radius can be adjusted in real time

29. 29 Adjustment Conditions AOI-radius decrease Number of connections > connection limits AOI-radius increase Maximum connections not exceeded Current AOI-radius < preferred AOI-radius Mutual awareness rule Do not disconnect a neighbor who sees me

30. 30 Demonstration Simulation video General movements (40 nodes, 800x600 world) Local vs. global view Dynamic AOI adjustment

31. 31 Outline Introduction Voronoi-based Overlay Network (VON) Simulation Results Analysis Conclusion

32. 32 Simulation Method C++ implementation of VON (open source VAST library) World size: 1200 x 1200, AOI: 100 Trials from 100 – 1000 nodes Connection limit per node: 20 1000 time-steps (~ 100 simulated seconds, assuming 10 updates/seconds) Behavior model Random movement:random destination Constant velocity: 5 units/step Movement duration: random (until destination is reached)

33. 33 Consistency Metrics Topology Consistency [Kawahara, 2004] observed AOI neighbors actual AOI neighbors Drift Distance [Diot, 1999] Distance between observed position and actual position (average over all nodes)

34. 34 Scalability: Avg. Transmission / sec

35. 35 Scalability: Max. Transmission / sec

36. 36 Scalability: Avg. Neighbor Size

37. 37 Consistency: Topology Consistency

38. 38 Consistency: Drift Distance

39. 39 Reliability: Effects of Packet Loss

40. 40 Outline Introduction Voronoi-based Overlay Network (VON) Simulation Results Analysis Conclusion

41. 41 Analysis of Design Scalability Bounded resource consumption  dynamic AOI Consistency (Topology) Topology is fully connected  enclosing neighbors Reliability Self-organizing  distributed neighbor discovery Responsiveness Lowest latency  direct connection, no relay

42. 42 P2P NVE Comparisons DHT- based Neighbor-list exchange SolipsisVON Consistency (topology) DHT & Supernode (consistent) Neighbor list- exchange (partitioning) Neighbor notify&query (undiscovery) Neighbor notify (consistent) Responsive- ness two to many One hop ScalabilityO(n) on supernode Constant in crowding Constant if fixed density Constant in crowding ConLatency too high Overlay partitioning Occasional undiscovery Circular node line-up

43. 43 Problems of Voronoi Approach Message traffic Circular round-up of nodes Redundant message sending (inherent to fully-distributed design) Incomplete neighbor discovery Can happen with inconsistent / incorrect neighbor list Fast moving node Limited AOI Direct connections

44. 44 Outline Introduction Voronoi-based Overlay Network (VON) Simulation Results Analysis Conclusion

45. 45 Summary NVE scalability is achievable with P2P architecture and is a neighbor discovery problem A promising solution: Voronoi-based P2P Overlay Leverage knowledge of each peer to maintain topology Properties Scalable: fully-distributed, dynamic AOI Efficient: low irrelevant messages, zero relay Simple: simple protocol and procedure

46. 46 Potential Applications Online games Position updates in current MMOGs, Voice-chats Military Enable large-scale, affordable military training simulation 3D Web Provide multi-user interactivity to static 3D world Scientific simulations Distribute spatial simulation requiring frequent synchronization

47. 47 Future Perspectives Short-term Distributed event/state consistency Customizable AOI (Heterogeneity in P2P) Recovery from overlay partition and fast-moving nodes Long-term Persistency issue(P2P-based database) Security issue (protection from malicious nodes) 3D content distribution (3D streaming on P2P) Massive, persistent 3D environment sharable by all!

48. 48 Acknowledgements Dr. Jui-Fa Chen ( 陳瑞發老師 ) Tsu-Han Chen( 鄭子涵 ) Members of the Alpha Lab, TKU CS Dr. Chin-Kun Hu( 胡進錕老師 ) Guan-Ming Liao( 廖冠名 ) LSCP, Institute of Physics, Academia Sinica Joaquin Keller(France Tele. R&D, Solipsis) Jon Watte(there.com) Kuan-Ta Chen( 陳寬達, NTU)

49. 49 Q&A Thank you! syhu@yahoo.com http://vast.sourceforget.net (http://vast.sf.net)

50. 50 Inconsistency caused by dAOI

51. 51 Topology Consistency in NLE

52. 52 Slow Discovery in Solipsis