1. Scaling a shared virtual environment — Presented by Junran Lei

2. What is in my presentation Some theories in the paper Comparisons with the methods in other systems/papers Better Solutions?

3. Content of the Paper How to increase scalability in the VE –Scalability — a measure of how well the system behaves when the number of users increases Technologies of relaxing consistency and hiding latency to increase scalability

4. Context of the Paper A running demonstration platform was derived from DIVE distributed virtual environment platform This is the previous research work for the “Community Place” – a shared multi-user VRML system designed by Sony Architecture

5. Scalability Issues Runtime extensibility Area of interest management Server architecture Components Network monitoring Security

6. Two Methods to Increase Scalability Increase Resource –using multiple servers? –more bandwidth? Decrease Consumption

7. How to reduce resource consumption Reducing message transmission –Reducing sharing Area of Interesting Management –Adaptive consistency Replicas of remote entities groups communication scripts to carry out local processing

8. The basic theory: Relax Consistency What makes the possibility of consistency relaxation in VE? –Human perceptual and cognitive limitations –Distributed VES are more concerned with visual notion, and perceptual consistency is more important than logical consistency

9. The basic theory: Relax Consistency What makes the possibility of consistency relaxation in VE? –Different consistency requirements are closely inter-related within a particular visual scene –Data access patterns for the majority of visual objects are simple

10. Reduce Sharing Area of Interest Management –Aura-- a sphere of interest associated with a user To partition the database Using dynamic aura to cut down the amount of information –Aura Manager To track the database partitions To control spatial, aural or sensory interaction

12. Why use Aura? How about other types of AOIM –Later…

13. Replicas of Remote Entities How to produce replica

14. Replicas of Remote Entities Differentiate data access to maintain the replicas –Static data — managed by read only objects –Dynamic data whose current value may be “out of date” — slow memory consistency model –Dynamic data that must always be “up to date” — maintained in a consistent manner

15. Replicas of Remote Entities Adaptive Consistency — Basic Model:Chaotic No Consistency Weakly consistency Stronger consistency Data Access Static data ‘out of date’ Dynamic data ‘up to date’ Dynamic data

16. Replicas of Remote Entities Scripts to support local replicated computation –To carry out local processing as the result of events –The extension of the technique dead reckoning

17. Group Communication Using group to representing the aura –Map the motion of auras to group Object joins the aura group when comes into the aura

18. Group Communication Using group for differing consistency requirements

19. Group Communication Adaptive Consistency — Basic Model:Chaotic No Consistency Weakly consistency Stronger consistency Three types of message delivery guarantees No Ordering Source Ordering Global Ordering

21. Group Communication Problem in group communication –Scarce group identifiers Solution: light weight group

22. Structure

23. The relationship between Aura, lightweight group and consistency group

24. Why use Aura? How about other types of AOIM

25. AOIM: Multiple Worlds Separate world connected through portals –Example systems: DIVE

26. AOIM: Static Spatial Subdivision Divide world surface into fixed size cells(shape) –Example systems: NPSNet

27. AOIM: Locales Areas of interest that map to physically divided spaces such as rooms in a building –Example systems: SPLINE

28. AOIM: Region Abstract regions mapped to application specific definitions of interest. –Example systems: MASSIVE II, DIVE (COVEN version)

29. AOIM: Aura Define a sphere of interest associated with a user. –Example systems:DIVE, MASSIVE I & II –focus : represents an observing object’s interest –nimbus : represents an observed object’s wish to be seen

30. AOIM: Aura nimbus must intersect with focus to receive

31. Why use Aura? Aura can be concerned not simply with space but also with aural or sensory interaction The size of a user’s aura can be dynamically reduced depending on the number of participants in the aura group

32. Why use Aura? Interaction between high speeding object and low speeding object in VE

33. Problems in Aura method 1) The system still requires considerable processing resources to transmit every object’s aura information The system cannot easily take advantage of network data dissemination efficiencies such as multicasting.

34. Possible Solutions 1) Using group communication

35. Problems in Aura method 2) The aura collision may occur but objects are unaware of this as a collision –Collision may not last for a sufficient amount of time to enable the DVE to ready the group membership details before objects move away from each other

37. Possible Solutions 2) Extending the fighter aircraft’s aura to enable such interaction? Predicted area of influence –To identify the extent of an object’s aura over a period of time –Given an object may travel in a straight line in any direction

40. Summary Increasing scalability from area of interesting management, adaptive consistency Using Predictive Interest Management to implement Aura technology

41. Future Work Better methods for AOIM Other consistency mechanisms Other possibilities of reducing message transmission or resource consumption

42. Reference Trends in Networked Collaborative Virtual Environments –Igor S. Pandzic, Chris Joslin, Nadia Magnenat Thalmann MIRALab – CUI, University of Geneva Collaborative Virtual Environments –Steve Benford, Chris Greenhalgh, Tom Rodden, and James Pycock DIVE: a scaleable network architecture for distributed virtual environments –Emmanuel Fr´econyand M°arten Steniusz, Swedish Institute of Computer Science

43. Reference Scalable Peer-to-Peer Networked Virtual Environment Matters of Scale –Manuel Oliveira, Computer Science Department, University College London Predictive Interest Management: An Approach to Managing Message Dissemination for Distributed Virtual Environments –Graham Morgan & Fengyun Lu Networked Virtual Environments / Design and Implementation –Sandeep Singhal and Michael Zyda / Ed