Mining Object Correlations to Improve Storage Performance in Virtual Environments

Object correlations are common semantic patterns in virtual environments (VE). They can be exploited for improve the effectiveness of storage caching, prefetching, data layout, and disk scheduling. Unfortunately, information about object correlations is unavailable at the VE level. However, little approaches for discovering object correlations in VE to improve the performance of storage systems. Moreover, current methods are presented for typical data mining datasets and not suitable for our virtual reality datasets. In this paper, we develop a class of view-based projection-generation method for mining various frequent sequential traversal patterns in the virtual environments. The frequent sequential traversal patterns are used to predict the user navigation behavior and help to reduce disk access time with proper placement patterns into disk blocks. Finally, we have done extensive experiments to demonstrate how these proposed techniques not only significantly cut down disk access time, but also enhance the accuracy of data prefetching.