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

Efficient AOI-Cast for Peer-to-Peer Networked Virtual Environments. Outline. Background Proposed schemes Evaluation Conclusion. Outline. Background Proposed schemes Evaluation Conclusion. Background. Networked Virtual Environment (NVE) Nodes or Avatars Coordinates

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

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

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

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

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

  5. 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 Adaptive Computing and Networking Laboratory Lab

  6. 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 scalabilitysince a user focuses on AOI neighbors. Adaptive Computing and Networking Laboratory Lab

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

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

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

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

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

  12. 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 Adaptive Computing and Networking Laboratory Lab

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

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

  15. 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 Adaptive Computing and Networking Laboratory Lab

  16. 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. Adaptive Computing and Networking Laboratory Lab

  17. VoroCast Adaptive Computing and Networking Laboratory Lab

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

  19. 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) Adaptive Computing and Networking Laboratory Lab

  20. FiboCast • Users inNVEs may pay more attention to activities that are moreobvious in the vicinity. • We can adaptively adjustthe transmission frequency so that neighbors with more hopcounts away receive messages less frequently. Adaptive Computing and Networking Laboratory Lab

  21. 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 <0, 1, 1, 2, 3, 5, 8,>, 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. Adaptive Computing and Networking Laboratory Lab

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

  23. 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 Adaptive Computing and Networking Laboratory Lab

  24. Simulation environment • 1 sec = 10 steps • Map = 1000 x 1000 (unit2) • 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 • F1 = 0；F2 = 1 Adaptive Computing and Networking Laboratory Lab

  25. Bandwidth consumption Adaptive Computing and Networking Laboratory Lab

  26. Neighborship consistency Adaptive Computing and Networking Laboratory Lab

  27. Drift distance Adaptive Computing and Networking Laboratory Lab

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

  29. 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 Adaptive Computing and Networking Laboratory Lab

  30. Q & A Adaptive Computing and Networking Laboratory Lab

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