peer-to-peer 3d streaming dissertation oral exam shun-yun hu department of computer science and...
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Peer-to-Peer 3D Streaming
Dissertation Oral Exam
Shun-Yun Hu
Department of Computer Science and Information EngineeringNational Central University
Dissertation Advisor: Prof. Jehn-Ruey Jiang
2009/11/17
IEEE INFOCOM 2008
IEEE INFOCOM 2008
IEEE INFOCOM 2008
Motivation
Two trends in virtual environments (VEs)Larger and more dynamic contentMore worlds
Content streaming is needed80% - 90% content is 3D (e.g., 3D streaming)
How to support millions of concurrent users?
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Imagine you start with a globe
Zoom in…
To Chung-Li
and NCU
Right now it’s flat…
But in the near future…
Outline
Introduction Background A Model for P2P 3D Streaming The Design and Evaluation of FLoD FLoD Extensions Discussions Conclusion
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What is 3D streaming?
Continuous and real-time delivery of 3D contentover network connections to allow user interactions without a full download.
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Object streaming
Hoppe 1996Progressive Meshes
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Scene streaming Multiple
objects Object
selection & transmission
Teler &Lischinski2001
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Visualization streaming Large volume Time-varying Resource
intensive
Olbrich & Pralle 1999
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Image-based streaming
Server-rendered
Thin clients Less
responsive
Cohen-Or et. al.2002
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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
The scalability problem
Client-server: has inherent resource limit
Resource limit
[Funkhouser95]
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A potential solution
Peer-to-Peer: Use the clients’ resources
Resource limit
[Keller & Simon 2003] 20/
Outline
Introduction Background A Model for P2P 3D Streaming The Design and Evaluation of FLoD FLoD Extensions Discussions Conclusion
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World model & area of interest (AOI)
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Model and assumptions
For a given object (mesh or texture)
All content is initially stored at a server
State managementSmall & updatable (~ KB)May require security / anti-cheatingEx. Avatar positions, health points, equipments
Content managementLarge & relatively static (~ MB)May authenticate via hashingEx. 3D polygonal models & textures
State vs. content management
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3D streaming requirements
Streaming qualityUser's perspective“how much?” & “how fast?”Speed
ScalabilityServer's perspectiveHow to offload?Concurrent users
Challenges for P2P 3D streaming
Distributed visibility determination Minimize server involvement Efficient determination without global knowledge
Dynamic group management Discovery of data sources Continuous avatar movements and real-time constrain
Peer & piece selection Optimal visual quality Content availability and bandwidth constrain
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A conceptual model
Pre-install: movement, rendering (client) 3D streaming: partition + fragmentation (server)
prefetching + prioritization (client) P2P: selection (client)
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P2P 3D streaming issues
Object discovery
Source discovery
State exchange
Content exchange
P2P video/file sharing
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Outline
Introduction Background A Model for P2P 3D Streaming The Design and Evaluation of FLoD FLoD Extensions Discussions Conclusion
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Observation
Users tend to cluster at hotspots Overlapped visibility = shared content
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Object discovery via scene descriptions
star: self triangles: neighborscircle: AOI rectangles: objects
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Source (neighbor) discovery via VON
Boundary neighbors
New neighbors
Non-overlapped neighbors
[Hu et al., IEEE Network, 2006]
Voronoi diagrams identify boundary neighbors for neighbor discovery
Flowing Level-of-Details (FLoD)
Object discovery: scene descriptions
Source discovery: VON
State exchange: query-response (pull)
Content exchange: random peer selection sequential piece
selection
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System architecture
Data flows
(A): scene request list (B): scene descriptions
(C): piece request list (D): object pieces
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Prototype experiment
Progressive models in a scene (by NTU) Peer-to-peer AOI neighbor requests (by NCU)
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Prototype experiment
Data 3D scene from a game demo (total ~50 MB)
Setup 100 Mbps LAN 10 participants, 48 logins captured in 40 min.
Results Found matching client upload & download Avg. server request ratio (SRR): 36%
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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 (~ 15 KB / object)
User behavior Random & clustering movement (1.5 * ln(n) hotspots)
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Simulation metrics
Scalability Bandwidth usage (Kbytes / sec) Server request ratio (% obtained from server)
Streaming quality Base latency (delay to obtain 1st piece) Fill ratio (obtained / visible data)
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Server bandwidth usage
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Client bandwidth usage (random)
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Client bandwidth usage (cluster)
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Effect of user density
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Fill ratio
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Base latency
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Effect of upload bandwidth
Outline
Introduction Background A Model for P2P 3D Streaming The Design and Evaluation of FLoD FLoD Extensions Discussions Conclusion
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Problems with basic FLoD
Source discovery: too few sources State exchange: pull may be slow Content exchange: better than random?
Real environment considerationsPeer heterogeneityBandwidth utilization
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FLoD enhancements
Enhanced peer & piece selection Wei-Lun Sung (ACM NOSSDAV’08)
Bandwidth-aware streamingChien-Hao Chien (ACM NetGames’09)
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Enhanced Selection
Proactive notification of availability (push) Periodic incremental exchange of content
availability information with neighbors.
Msg_Type Obj_ID Max_PID Obj_ID Max_PID ‧‧‧‧
incremental content information
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Multi-Level AOI Request
Localized requests may prevent contentions Peers request from closer neighbors/levels first
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Compare enhanced strategy with FLoD
Simulation Environment
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Base Latency
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Fill ratio
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Bandwidth-aware Peer Selection
Region-based Peer List to increase sources Pre-allocation of connection channels
Multi-source peer selection Channel neighbors (bandwidth reservation) AOI neighbors (no response guarantee) Server (no response guarantee)
Tit-for-Tat peer selection (from BitTorrent) Channel-neighbor first Higher contributor first
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Simulation environment
World Size 1000 x 1000 (units)
Cell Size 100 x 100 (units)
AOI Radius 100 (units)
Time steps 1500 (steps/ sec)
Object Data Size Range
100 – 300 (KB)
% of Base Piece 10%
Refinement Piece Size
5 (KB)
Server Bandwidth Download/Upload
1000/ 1000 (KB/sec)
User Bandwidth Distribution
Downlink (KB/sec)
Uplink (KB/sec)
Fraction of
nodes96 10 0.05
187 30 0.45375 100 0.40
1250 625 0.10
[Bharambe et al, 2006]
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Streaming quality (= BW utilization)
100 to 500 objects, fixed at 100 peers
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System scalability
50 to 450 peers, fixed 300 objects
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Fill ratio time-series (QoS)
original FLoD Enhanced
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Outline
Introduction Background A Model for P2P 3D Streaming The Design and Evaluation of FLoD FLoD Extensions Discussions Conclusion
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LODDT (Cavagna et al. 2006)
‧ ‧‧
‧
‧
Object Tree Node Aura
U
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HyperVerse (Botev et al, 2008)
Backbone + overlay architecture
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Comparisons
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Outline
Introduction Background A Model for P2P 3D Streaming The Design and Evaluation of FLoD FLoD Extensions Discussions Conclusion
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Summary
P2P 3D streaming has four main issues Object discovery Source discovery State exchange Content exchange
FLoD demonstrates that P2P allows Much lower server resource usage Better performance in crowding
FLoD’s performance can be enhanced with Pushed-based state exchange Pre-allocated fixed-size bandwidth channels
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Conclusion
3D streaming could become an important net traffic Non-sequential access Latency-sensitive
Peer-to-peer streaming is promising Reduce server resource usage Dynamic interest groups
New area with many interesting issues Graphics: progressive encoding / decoding, compression Networking:group discovery, prefetching, topology, versioning
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Future works
Practical AdoptionsDynamic content updateTopology-aware P2P 3D streamingSecure P2P 3D streaming
Open questionsMany small worlds vs. one large worldHigh-definition (HD) contentIncentives & killer apps
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FLoD publications
1. Shun-Yun Hu, "A Case for 3D Streaming on Peer-to-Peer Networks," in Proc. ACM Web3D, Apr. 2006, pp. 57-63.
2. Shun-Yun Hu, Ting-Hao Huang, Shao-Chen Chang, Wei-Lun Sung, Jehn-Ruey Jiang, and Bing-Yu Chen, "FLoD: A Framework for Peer-to-Peer 3D Streaming," in Proc. IEEE INFOCOM, pp. 1373-1381, Apr. 2008.
3. Wei-Lun Sung, Shun-Yun Hu, and Jehn-Ruey Jiang, "Selection Strategies for Peer-to-Peer 3D Streaming," in Proc. NOSSDAV, May. 2008.
4. Chang-Hua Wu, Shun-Yun Hu, and Li-Ming Tseng, "Discovery of Physical Neighbors for P2P 3D Streaming," in Proc. ICUMT, Oct. 2009.
5. Mo-Che Chan, Shun-Yun Hu, and Jehn-Ruey Jiang, "Secure Peer-to-Peer 3D Streaming," Multimedia Tools and Applications, vol. 45, no. 1-3, Oct. 2009, pp. 369-384.
6. Chien-Hao Chien, Shun-Yun Hu, and Jehn-Ruey Jiang, "Bandwidth-Aware Peer-to-Peer 3D Streaming," in Proc. NetGames, Nov. 2009.
7. Shun-Yun Hu, Jehn-Ruey Jiang, and Bing-Yu Chen, "Peer-to-Peer 3D Streaming," IEEE Internet Computing, to appear, 2009.
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Q & A
Thank you!
http://ascend.sourceforge.net
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Related work 3D streaming
Progressive meshes [Hoppe 96] Geometry image [Gu et al. 02] Scene streaming [Teler and Lischinski 2001]
P2P media streaming Zigzag, oStream, Coolstreaming, Prime
Nonlinear media streaming Channel Set Adaptation (CSA) [Gotz, 2006]
P2P 3D streaming LOD-DT [Cavagna et al. 2006]
Secure P2P 3D streaming
How to authenticate content from untrusted peers?
Four types of contentWhole model (digital signature)Linear stream (hash chain)Independent stream (Rabin-based)Partially linear stream (hash DAG)
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Cache utilization
Experimental results
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Extended Candidate Buffer Non-AOI neighbors may still possess data Maintain extra list of non-AOI neighbors
R
S Obj
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