Research Of The Distributed And Hierarchical Data Resource Discovery Approach

Nowadays, Grid Computing is a hot research topic in distributed computing area. Resource management is acknowledged as one of the main points of Grid Computing, and resource discovery is a basic issue in Grid resource management, which concerns discovering resources in Grid to meet the requirement of applications. With the rapid development of Grid, centralized Grid resource discovery schemes have potential scalability and performance problems. Therefore, in order to survive the dynamic and larger-scale Grid environment, resource discovery should be decentralized and should not rely on centralized control.Data Grid is one of most important Grids, and it provides sharing of both data. resources and the capability of data processing globally. The thesis thus focuses on the Data Grid, and presents a distributed and hierarchical data resource discovery approach. Its distinguished feature is the high efficiency of the resource discovery process while keeping completely distributed.In details, the thesis makes following contributions:1. The framework of the distributed and hierarchical data resource discovery approach.The thesis first constructs the theoretical model for data resource discovery. Then a hierarchical framework for data resource discovery, as well as the comparison with current methods, is presented. The presented framework may effectively control the propagation of messages.2. The metadata-based resource type index network.Different types of data resources are organized into different resource sub-networks based on their metadata. We also discuss the topics on distributed metadata nodes discovery, topology construction and maintenance, metadata registration, and metadata request forwarding.3. The resource locating method inside the underlaid data resource network based on local information.Such resource locating method is used to find out the optimal data resource inside the underlaid data resource network. An analysis model is built to study the effectiveness of the presented method, and results are also validated by simulation. It is shown that, this method can find resources with relative high qualities among all qualified resources in a big resource network with small number of hops.