1 Scalable Peer-to-Peer Virtual Environments Shun-Yun Hu ( 胡舜元 ) CSIE, National Central University, Taiwan 2008/05/26

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Massively Multiplayer Online Games MMOGs are growing quickly Multi-billion dollar industry 10 million subscribers for World of Warcraft 600,000 concurrent users, but 3,000 per world Can we scale to millions in the same world?

Imagine you start with a globe

Zoom in…

To Mannheim…

and to Universitat Mannheim

Right now it’s flat…

But in the near future…

Virtual Environments (VEs): A shared space

14 Model for virtual worlds Many nodes on a 2D plane Message exchange with those within Area of Interest (AOI)‏ How does each node receive the relevant messages? Area of Interest

15 A simple solution (point-to-point)‏ But…too many irrelevant messages N * (N-1) connections ≈ O(N 2 )  Not scalable! Source: [Funkhouser95]

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

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

The Problem Client-server: resources limited by provisioning Resource limit [Funkhouser95]

The Solution Peer-to-Peer: resources grow with demand Resource limit [Keller & Simon 2003]

Voronoi-based Overlay Network (VON)‏

Design Goals Observation: for VEs, the contents are messages from AOI neighbors Content discovery is a neighbor discovery problem Specific goals: Scalable  Limit per-node message traffic Responsive  Direct connection with AOI neighbors

22 If you talk with your AOI Neighbors directly… But how to discover new neighbors?

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 Design Concepts Identify enclosing and boundary neighbors Enclosing neighbors are minimally maintained Mutual collaboration in neighbor discovery boundary neighbor (B.N.)‏L. Blue unknown neighborL. Green normal AOI neighborGreen E.N. & B.N.Pink enclosing neighbor (E.N.)‏Yellow selfWhite Area of Interest (AOI)‏Circle Use Voronoi to solve the neighbor discovery problem

25 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 neighbors Boundary neighbors New neighbors Non-overlapped neighbors

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

27 Demonstration Simulation demo Random movements (100 nodes, 1200x700 world)‏ Local vs. global view Dynamic AOI adjustment

Simulations C++ implementation of VON (open source VAST library) World size: 1200 x 1200 (AOI: 100) Trials from 200 – 2000 nodes Connection limit: time-steps (~ 300 simulated seconds, assuming 10 updates/seconds) Behavior model Random movement:random waypoint Constant velocity: 5 units/step Movement duration: random (until destination is reached)

Scalability: Avg. Transmission / sec

Scalability: Max. Transmission / sec

Extension: VoroCast Pack reduction via forwarding Headers reduction Data compression & aggregation

Voronoi State Management (VSM)‏

33 Voronoi State Management VON deals with positions only, but states stored in spatial objects (with x, y) are important too. Let game states be managed by all clients Each client has two roles: peers & arbitrators i.e. Voronoi partitioning Three problems: O(n 2 ) connections at hotspots Some cells have large sizes Constant ownership transfer

34 VSM: solution ideas Connection overload→ Aggregators clustering Large cell-size → Virtual peers incremental transfer Constant transfers→ Explicit ownership transfer

35 VSM: Consistency control Managing arbitrator receives and processes events Events are forwarded if necessary Updates sent to affected arbitrators, then peers

36 VSM: Consistency control Managing arbitrator receives and processes events Events are forwarded if necessary Updates sent to affected arbitrators, then peers

37 VSM: Consistency control Managing arbitrator receives and processes events Events are forwarded if necessary Updates sent to affected arbitrators, then peers

38 VSM: Consistency control Managing arbitrator receives and processes events Events are forwarded if necessary Updates sent to affected arbitrators, (then peers)

39 VSM: Load balancing Traditional: high-capacity nodes first, then adjust VSM: low-capacity nodes first, then cluster Overload: ask for aggregator, submit control Underload: disintegrate, release control

Peer-to-Peer 3D Streaming

41 Background MMOGs today need DVD installations Too slow and unpractical for: Larger and more dynamic worlds (TBs data) More numerous worlds (Web 3D) Content streaming is needed 80% - 90% content is 3D (e.g., 3D streaming)

42 3D streaming Object streaming [Hoppe 1996] base refinements Scene streaming [Teler & Lischinski 2001] multiple objects object selection & prioritization

43 3D streaming vs. media streaming Video / audio media streaming is very matured User access patterns are different for 3D content Highly interactive  Latency-sensitive Behaviour-dependent  Non-sequential Analogy Constant & frequent switching of multiple channels

Challenges for P2P 3D streaming Appropriate peer grouping Matching interests / needs Matching capabilities Dynamic group management Interest groups are dynamic(non-sequential) Real-time constraints(latency-sensitive) Minimal server involvement Visibility determination (object selection) Request prioritization (piece selection)

45 Observation Limited & predictable area of interest (AOI)‏ Overlapped visibility = shared content

46 overlapped visibility = shared content

47 Download content from AOI neighbors star: selftriangles: neighbors circle: AOIrectangles: objects

48 Neighbor discovery via VON Boundary neighbors New neighbors Non-overlapped neighbors [Hu et al. 06] Voronoi diagrams identify boundary neighbors for neighbor discovery

49 Prototype experiment Progressive models in a scene (by NTU) Peer-to-peer AOI neighbor requests(by NCU) Found matching client upload / download

50 Simulation setup Environment 1000x1000 world, 100ms / step, 3000 steps client: 1 Mbps / 256 Kbps, server: 10 Mbps (both)‏ Objects Random object placement (500 objects)‏ Object size based on prototype User behavior Random & clustering movement (1.5 * ln(n) hotspots)‏

51 Server bandwidth usage

52 Client bandwidth usage

Impacts of P2P VEs… No server as bottleneck  scalable Commodity hardware  affordable 2D web  3D web Earth-scale virtual worlds (millions/billions of people)‏

Unresolved issues Overlay management Topology-aware, capacity-matching superpeers Flexible publication / subscription Direct vs. forwarding deliveries State management Load balancing (high user density) Persistency Security Client-assisted services (e.g., P2P 3D streaming) Source nodes discovery Visualization vs. networking priority matching LOD considerations

Meshing physical & virtual topologies Client 2 Client 1

A generic pub/sub scenario pub sub

Other issues Common API Shared simulator / platform Interoperability

58 Q&A VON: A Scalable Peer-to-Peer Network for Virtual Environments IEEE Network, vol. 20, no. 4, Jul./Aug FLoD: A Framework for Peer-to-Peer 3D Streaming IEEE INFOCOM, Apr Thank you!