Prediction-based concurrency control for a large scale networked virtual environment supporting various navigation speeds

  • Authors:
  • Eunhee Lee;Dongman Lee;Seunghyun Han;Soon J. Hyun

  • Affiliations:
  • Information and Communications University, Taejon, Korea;Information and Communications University, Taejon, Korea;Information and Communications University, Taejon, Korea;Information and Communications University, Taejon, Korea

  • Venue:
  • VRST '01 Proceedings of the ACM symposium on Virtual reality software and technology
  • Year:
  • 2001

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Abstract

Shared sense of a virtual world is often enhanced by replicating the information at each user's site since replication provides acceptable interactive performance, especially when users are geographically distributed over large networks like the Internet. However, multiple concurrent updates may lead to inconsistent views among replicas. Therefore concurrency control is a key factor to maintaining a consistent state among replicas. We proposed a scalable prediction-based scheme in which an ownership request is multicasted to only the users surrounding a target entity. In our previous work, we assumed that all the users navigate a virtual world with a single speed. It, however, is quite common in a networked virtual environment like a network game that users are allowed to change their navigation speed as they interact with a virtual world for adding more realism. This paper proposes an enhancement to support users with various speeds. The enhanced scheme allows as many Entity Radii as the number of different speed and allocates a separate queue for users of each speed. Each queue is examined in parallel to predict the next owner candidate and among the selected candidates is chosen the final candidate, which has a minimum predicted collision time. It contributes to the timely advanced transfer of ownership by using appropriate Entity Radius based on a user's speed, fair granting of ownership by reducing the interference between users with different speed and latency, and high prediction accuracy by reducing the redundant ownership transfer.