Knowledge_based Object Placement in Virtual Environments

As the advance of computer graphics architecture increases, the demand for moving data between storage systems are necessary and frequently, especially in virtual environments (VE). The current researches still depend on the spatial locality. The object correlations are always neglected and absent. However, very little approaches were proposed for exploiting object correlations in VE to improve the performance of storage systems. In this paper, we develop a class of knowledge-based hypergraph-model for clustering various frequent sequential traversal patterns in the VE. The frequent sequential traversal patterns are used to predict the user navigation behavior. Furthermore, the hypergraph (HG)-based clustering scheme can help 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.