1. Managing Dynamic Shared Space
In Networked Virtual Environments
Pallav Dhobley 09005012
Vihang Gosavi 09005016
Aditya Gupta 09005017
Ashish Yadav 09005018

2. Contents:
• Introduction & Motivation
• Dynamic Shared State
• Consistency-Throughput tradeoff
• Managing Shared States
• Conclusion
• References

3. Introduction & Motivation
• The fundamental goal of a net-VE is to provide
user with the illusion that they are all seeing
the same things and interacting with each
other within that virtual space

4. What is Dynamic Shared State?
• Dynamic information maintained by multiple
hosts about net-VE
• Common context
• Makes VE truly “realistic” & “multi-user”
• Managing it is the most challenging part of
building a net-VE

5. Virtual World Virtual World
Model of Player1 Model of Player2
Player1 Player2
State change and
Interaction event
messages
Network

6. The Problem
Throughput • Consistency-Throughput Tradeoff:
– the fundamental rule of net-VE shared
Real-time Scalable
state:
“it is impossible to allow dynamic
shared state to change frequently and
Reliable
guarantee that all hosts
simultaneously access identical
Constancy versions of that state.”
We can either have a Dynamic world or a consistent world, but not both.

7. Managing Shared States
More Consistency
• Shared Repositories
– All from the same well (Data Server)
• Blind Broadcasting
– Talk a lot! (Network Messages)
• Dead Reckoning
– Predict the future! (State estimation)
More Dynamic

8. Shared Repositories
• Maintaining shared state in centralized
repositories
• Using “lock” on data to ensure synchronization
Three techniques of centralised repositories :
1. Shared file directory
2. Repository in server memory
3. Distributed repository/ Virtual repository

9. Shared file Directory
• Directory containing files that hold shared
states.
– Absolute Consistency!
– One host can write data to same file at a time
– Scalability issues
– Slow! 

10. Shared file Directory

11. Server memory
• Server process which simulates the behavior
of distributed file system
– Faster than Shared File Directory
– No need of locks, server arbitrates
– Server-single point of failure
– Bottleneck: Server Bandwidth
– Need to maintain constant connection

12. Server memory

13. Virtual Repository
• Hosts communicate directly with each other
following a protocol of information sharing
– Reduced bottleneck at server
– Better fault tolerence
– “Eventual” consistency

14. Virtual Repository

15. Blind Broadcasting
• Asynchronous broadcasting of owned states at
regular intervals
• Clients cache the most recent update
• Frequency compensates for data-lost.
• Explicit Object ownership
• Filter: Broadcast to those who are “seen”
– VEOS epidemic approach: send-to-neighbors

16. Blind Broadcasting
• Can support a larger number of users at a
higher frame rate and faster response time.
• Simple to implement
• Jitter may lead to “jerky” visual behavior

17. Dead Reckoning
• Transmit state updates less frequently by
using past updates to estimate the shared
state
• Prediction: Estimation of current state based
on previously received packets
• Convergence: correction of estimated state
on arrival of new packet

18. Dead Reckoning
• No need of central server
• Trading accuracy of shared state for more
scalability

19. Dead Reckoning
• Prediction:
– Using derivative polynomials
• Zero order derivative
f(t+t0) = f(t) where f(t) is location at time “t”
• First order derivative
f(t+t0)=f(t)+v(t)*t0 where v(t) is velocity at time “t”
• Hybrid polynomial

20. Dead Reckoning
• Convergence: Algorithms
– Zero order
• Jumping from predicted position to corrected position
– First order
• Linear transition from predicted position to actual
position
• Choice of convergence algorithm may vary
with the requirements

21. Dead Reckoning
• Reduces bandwidth requirements
• Can support large number of players
• Calculations are done at the host,
independently
• Complex to develop and implement
• Not all hosts share the identical state about
each entity

22. Conclusion
• Choice of shared share maintenance
technique is a task that must balance a variety
of issues including
– Bandwidth
– Computation
– Latency
– Data consistency
– Reproducibility

23. Conclusion(ctd)
• Shared state maintenance is governed by the
consistency throughput tradeoff

24. References
• Singhal, Sandeep and Zyda Michael. Networked Virtual
Environments, New York: ACM Press. PP. 101-146
• Zona Inc., and Executive Summary Consulting Inc, “State of
MMOG 2002”, October 2002
• Anupam, V., and C. Bajaj. Distributed and collaborative
visualization , IEEE Multimedia 1(2):39-49,Summer 1994.
• http://web.ntpu.edu.tw/~jyhuang/ National Taipei
University
• Carlsson,C., and O. Hagsand. DIVE- A platform for multi-
user virtual environments. Computers and Graphics
17(6):663:669 November-December 1993.