Data resource discovery model based on hybrid architecture in data grid environment
Tinghuai Ma, Yinhua Lu, Sunyuan Shi, Wei Tian, Xin Wang, Donghai Guan · Concurrency and Computation Practice and Experience · 2014
Summary Today, the management of massive data collections draws much attention as data grids have been developed to deal with large computational problems and provide the opportunity for sharing geographically distributed resources for large‒scale data‒intensive applications. Therefore, finding an effective approach to discover data resources in order to promote better interactions between application communities or virtual organizations becomes a critical challenge. Traditional grid resource discovery models are mostly based on central and hierarchical architecture that can lead to bottlenecking with the expansion of the grid scale. Although the Peer‒to‒Peer (P2P) technique is integrated into the grid in order to improve the performance in recent years, each P2P structure still has drawbacks that require several compensatory strategies. In this paper, based on the unstructured super‒node‒based architecture from the P2P system, we design a structured logic resource tree in each domain in order to effectively alleviate the load on the super‒node, and we propose a query recording learning algorithm based on this hybrid architecture to reduce traffic in the network and greatly shorten the response time. The model and algorithm are validated by simulations and compared with the traditional super‒peer model and the flooding‒based approach. Copyright © 2014 John Wiley & Sons, Ltd.