scalable data management using user-based caching and prefetching in distributed virtual environments
For supporting real-time interaction in distributed virtual environments (DVEs), it is common to replicate virtual world data at clients from the server. For efficient replication, two schemes are used together in general - prioritized transfer of objects and a caching and prefetching technique. Existing caching and prefetching approaches for DVEs exploit spatial relationship based on distances between a user and objects. However, spatial relationship fails to determine which types of objects are more important to an individual user, not reflecting user's interests. We propose a scalable data management scheme using user-based caching and prefetching exploiting the object's access priority generated from spatial distance and individual user's interest in objects in DVEs. We also further improve the cache hit rate by incorporating user's navigation behavior into the spatial relationship between a user and the objects in the cache. By combining the interest score and popularity score of an object with the spatial relationship, we improve the performance of caching and prefetching since the interaction locality between the user and objects are reflected in addition to spatial locality. The simulation results show that the proposed scheme outperforms the hit rate of existing caching and prefetching by 10\% on average when the cache size is set to basic cache size, the size of expected number of objects included in the user's viewing range.
scalable data management caching and prefetching distributed virtual environments dves user interest