Data resource discovery model based on hybrid architecture in data grid environment

摘要

Today, the management of massive data collections draws much attention as data grids have been developedrnto deal with large computational problems and provide the opportunity for sharing geographically distributedrnresources for large-scale data-intensive applications. Therefore, finding an effective approach to discoverrndata resources in order to promote better interactions between application communities or virtual organizationsrnbecomes a critical challenge. Traditional grid resource discovery models are mostly based on centralrnand hierarchical architecture that can lead to bottlenecking with the expansion of the grid scale. Although thernPeer-to-Peer (P2P) technique is integrated into the grid in order to improve the performance in recent years,rneach P2P structure still has drawbacks that require several compensatory strategies. In this paper, based onrnthe unstructured super-node-based architecture from the P2P system, we design a structured logic resourcerntree in each domain in order to effectively alleviate the load on the super-node, and we propose a queryrnrecording learning algorithm based on this hybrid architecture to reduce traffic in the network and greatlyrnshorten the response time. The model and algorithm are validated by simulations and compared with therntraditional super-peer model and the flooding-based approach.