P2P Network for Very Large Virtual Environment Proceedings of the ACM symposium on virtual reality...

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P2P Network for Very Large Virtual Environment Proceedings of the ACM symposium on virtual reality software and technology VRST '06
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Transcript of P2P Network for Very Large Virtual Environment Proceedings of the ACM symposium on virtual reality...

P2P Network for Very Large Virtual Environment

Proceedings of the ACM symposium on virtual reality software and technology VRST '06

Adaptive Computing and Networking Lab

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Outline

Introduction Overview System Structure Hierarchical LOD Object Structure Data Exchange Methods Simulation Conclusion

Adaptive Computing and Networking Lab

Introduction A centralized architecture is obviously not

a good framework to build a truly scalable Virtual Environment (VE).

P2P network overlays are potentially proper architecture.

Adaptive Computing and Networking Lab

Introduction This paper describes a peer connectivity

method for visualizing very vast and complex environments and sharing these environments among many users.

Adaptive Computing and Networking Lab

Adaptive Computing and Networking Lab

Key Concept

Neighbor peers in a VE are likely to have a lot of data in common

For example, if two peers have nearly the same viewpoint in a city model, data stored in both peers will be almost the same.

Adaptive Computing and Networking Lab

Challenges

Finding and maintaining the appropriate peer connectivity is a very difficult problem in a changing environment where peers viewpoint are allowed to move freely and peers can disconnect or appear at any time.

Adaptive Computing and Networking Lab

Adaptive Computing and Networking Lab

Peer Connectivity Method

Self-Adaptation:the ability for a requesting peer to dynamically take into account the serving capacity of neighbor peers

Adaptive Computing and Networking Lab

Peer Connectivity Method

Three types of peers:Memory Peers (MP)

Likened to central servers

Visualization Peers (VP) End-user peers

Connectivity Peers (CP) Assigned the task of achieving the connectivity bet

ween peers

Adaptive Computing and Networking Lab

Peer Connectivity Method

Each CP manages all VPs connected to it and, upon a request of a connected VP, it provides a list of the N VP neighbors, N being a parameter of the request.

The list of VP neighbors is the allocated serving pool (ASP).

Adaptive Computing and Networking Lab

Adaptive Computing and Networking Lab

Peer Connectivity Method

Goal:to distribute the data requests of a VP among its neighbor VPs.

Adaptive Computing and Networking Lab

Peer Connectivity Method

The selection of the serving VPs is made according to the following characterizes:Time To Server (TTS).The estimated available dataThe upload limit in bandwidth (BPUPL) assigned

to this peer for P2P serving tasks.The number of peers currently served by the

serving peer (NBPCS).

Adaptive Computing and Networking Lab

Simulation Accuracy:

ACC = NMISS / NREQ

Accuracy at time t is measured as the number of missing nodes (NMISS) in the level-of-detail (LOD) trees with respect to the number of required nodes (NREQ) to render the current viewpoint.

Adaptive Computing and Networking Lab

Simulation

Adaptive Computing and Networking Lab

Progressive Building Tree: PBTree

Adaptive Computing and Networking Lab

LOD Description Tree

Adaptive Computing and Networking Lab

Using LODDT to render different viewpoints

Adaptive Computing and Networking Lab

Using LODDT to assess the content of neighbor peer cache

Adaptive Computing and Networking Lab

Data Exchange Methods

Adaptive Computing and Networking Lab

Data Exchange Methods

Four types data exchange methods:From the MP only (C/S)From the peer having the least TTSFrom the closest peerUsing self-adaptive distribution method.

Adaptive Computing and Networking Lab

Simulation

Adaptive Computing and Networking Lab

Simulation

Adaptive Computing and Networking Lab

Simulation

Adaptive Computing and Networking Lab

Conclusion This paper proposes a new spatially-organized P

2P network overlay with intent to obtain self-scalability for a truly scalable Virtual Environment (VE).

It takes into account peer proximity along with some parameters and state variables that allow to assess the current serving capacity of the neighbor peers.