Scalable
Peer-to-Peer Virtual Environments
Shun-Yun Hu
CSIE, National Central University, Taiwan
2008/06/03
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2
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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 Trier…
and to Universitat Trier
Right now it’s flat…
But in the near future…
Virtual Environments (VEs):
A shared space
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
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A simple solution (point-to-point)
Source: [Funkhouser95]
But…too many irrelevant messages
N * (N-1) connections ≈ O(N2)  Not scalable!
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A better solution (client-server)
Source: [Funkhouser95]
Message filtering at server to reduce traffic
N connections = O(N)  server is bottleneck
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Current solution (server-cluster)
Source: [Funkhouser95]
Still limited by servers.
Expensive to deploy & maintain.
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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]
Outline

Overlay management
(VON)

State management
(VSM)

Client-assisted service
(FLoD)
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
If you talk with your AOI Neighbors directly…
games can be built
But how to discover new neighbors?
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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
Region
Site
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Design Concepts
Use Voronoi to solve the neighbor discovery problem


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Identify enclosing and boundary neighbors
Enclosing neighbors are minimally maintained
Boundary neighbors mutually help to discover neighbors
Circle
Area of Interest (AOI)
White
self
Yellow
enclosing neighbor (E.N.)
L. Blue
boundary neighbor (B.N.)
Pink
E.N. & B.N.
Green
normal AOI neighbor
L. Green unknown neighbor
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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
Non-overlapped
neighbors
Boundary
neighbors
New
neighbors
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Dynamic AOI
Crowding within AOI can overload a particular node
It’s better if AOI-radius can be adjusted in real time
Demonstration
Simulation demo
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Random movements (100 nodes, 1200x700 world)
Local vs. global view
Dynamic AOI adjustment
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Simulations
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C++ implementation of VON (open source VAST library)
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World size:
Trials from
Connection limit:
3000 time-steps
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1200 x 1200
200 – 2000 nodes
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(AOI: 100)
(~ 300 simulated seconds, assuming 10 updates/seconds)
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Behavior model
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Random movement:
Constant velocity:
Movement duration:
random waypoint
5 units/step
random (until destination is reached)
Scalability: Avg. Transmission / sec
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basic
dAOI
basic (fixed density after 1000 nodes)
dAOI (fixed density after 1000 nodes)
Size (kb /
25
20
15
10
5
0
0
400
800
1200
Number of Nodes
1600
2000
Scalability: Max. Transmission / sec
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basic
dAOI
basic (fixed density after 1000 nodes)
dAOI (fixed density after 1000 nodes)
Size (kb /
60
50
40
30
20
10
0
0
400
800
1200
Number of Nodes
1600
2000
Voronoi State Management
(VSM)
State Management for VEs
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Besides positions, object states are important too
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Three main issues:
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Consistency control
Load balancing
Fault tolerance
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A basic approach
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Let game states be managed by all clients
Each client has two roles: peers & arbitrators
i.e., Voronoi partitioning (we can use VON)
Problems with basic approach
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O(n2) connections at hotspots
Some cells have large sizes
Constant ownership transfer
VSM: solution ideas
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Connection overload
Large cell-size
Constant transfers
→ Aggregators clustering
→ Virtual peers incremental transfer
→ Explicit ownership transfer
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VSM: Consistency control
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Managing arbitrator
receives and
processes events
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Events are forwarded
if necessary
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Updates sent to
affected arbitrators,
then peers
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VSM: Consistency control

Managing arbitrator
receives and
processes events

Events are forwarded
if necessary

Updates sent to
affected arbitrators,
then peers
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VSM: Consistency control

Managing arbitrator
receives and
processes events

Events are forwarded
if necessary

Updates sent to
affected arbitrators,
then peers
39
VSM: Consistency control

Managing arbitrator
receives and
processes events

Events are forwarded
if necessary

Updates sent to
affected arbitrators,
(then peers)
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VSM: Load balancing
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Traditional:
VSM:
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Overload:
Underload:
high-capacity nodes first, then adjust
low-capacity nodes first, then cluster
ask for aggregator, submit control
disintegrate, release control
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VSM: Fault tolerance
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Regular arbitrator:
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Pick backup arbitrator, backup states
Backup transfers ownership to enclosing arbitrators
Aggregators:
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Pick backup aggregators
Take over original if failed
Choose new backup
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Peer-to-Peer 3D Streaming
Background
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MMOGs today need DVD installations
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Too slow and unpractical for:
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Larger and more dynamic worlds (TBs data)
More numerous worlds
(Web 3D)
Content streaming is needed
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80% - 90% content is 3D (e.g., 3D streaming)
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3D streaming
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Continuous and real-time delivery of 3D content to
allow user interactions without a full download.
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base & refinement pieces
Refinements
Base
1
2
3
(Hoppe 96)
User
Scene streaming
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Multiple objects
Object determination & prioritization
[Teler &
Lischinski 2001]
3D streaming vs. media streaming
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Video / audio media streaming is very matured
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User access patterns are different for 3D content
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Highly interactive
Behaviour-dependent

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Latency-sensitive
Non-sequential
How to scale to millions of concurrent users?
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Observation
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Limited & predictable area of interest (AOI)
Overlapped visibility = shared content
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overlapped visibility = shared content
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Challenges for P2P 3D streaming
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Appropriate peer grouping
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Dynamic group management
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Matching interests / needs
Matching capabilities
Interest groups are dynamic
Real-time constraints
(non-sequential)
(latency-sensitive)
Minimal server involvement
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Visibility determination
Request prioritization
(object selection)
(piece selection)
FLoD [Infocom 2008]
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VE partitioned into cells with scene descriptions
Assumes P2P overlay that provides AOI neighbors
star:
circle:
self
AOI
triangles:
neighbors
rectangles: objects
Neighbor discovery via VON
Voronoi diagrams identify boundary neighbors for
neighbor discovery
Non-overlapped
neighbors
Boundary
neighbors
New
neighbors
[Hu et al. 06]
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Prototype experiment
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Progressive models in a scene
(by NTU)
Peer-to-peer AOI neighbor requests
(by NCU)
Found matching client upload / download
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Simulation setup
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Environment
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Objects
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1000x1000 world, 100ms / step, 3000 steps
client: 1 Mbps / 256 Kbps, server: 10 Mbps (both)
Random object placement (500 objects)
Object size based on prototype
User behavior
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Random & clustering movement (1.5 * ln(n) hotspots)
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Server bandwidth usage
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Client bandwidth usage
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Impacts of P2P VEs…
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No server as bottleneck
Commodity hardware
2D web  3D web
Earth-scale virtual worlds
(millions/billions of people)
 scalable
 affordable
Unresolved issues
Issues for creating VEs
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Consistency
Interactivity
Security
multiplayer
Scalability
Persistency
Reliability
massively multiplayer
Interoperability
Content
3D web
Meshing physical & virtual topologies
Client 2
Client 1
A generic pub/sub scenario
pub
sub
Q&A
VON: A Scalable Peer-to-Peer Network for Virtual Environments
IEEE Network, vol. 20, no. 4, Jul./Aug. 2006
FLoD: A Framework for Peer-to-Peer 3D Streaming
IEEE INFOCOM, Apr. 2008
Thank you!
http://vast.sourceforge.net
http://ascend.sourceforge.net
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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
Other issues
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Common API
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Shared simulator / platform

Interoperability
Extension: VoroCast
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Pack reduction via forwarding
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Headers reduction
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Data compression & aggregation