| At present, Grid and P2P Computing are two hotspots in Distributed Computing researchfield. Grid and P2P Computing apply different technologies, however, they are both aimed atsharing and collaborating hardware and software distributed in network. Recently, there is atrend that Grid and P2P Computing merge in research and practice fields.In solving the existing problems in Grid and P2P Computing fields, Ian Foster pointed outthat Grid had a comparatively perfect structure, but it couldn't solve problems such as single-node failure and related data and service failure perfectly; while P2P could adapt well to thenode changed dynamically, but it hadn't good system architecture[1]. Whether we can mergethose advantages and then give solutions to service failure, resource locating and informationsearching becomes a current hotspot and difficulty in distributed computing field.Focusing on this problem, this paper studies intensively the problem of Grid and P2P'smerging with theories and practices. Firstly, Large-Scale Systems Cybernetics[2]is as a guide,and GriP model based on P2P and Grid is put forward. This model combines good architectureand robust node fault recovery; Secondly, it presents P-SRing method, is a solution to nodefailure in grid; Finally, after achieving GriP's topology stability, resource sharing mechanismand efficient recover algorism in RMS (Resource Management System), which are based onmeta-data, are proposed.The main contents and innovations are as follows:I. Putting forward a distributed network computing model---GriP, based on P2P and Grid.Nodes in Grid are relative stable, but Grid can't solve single-node failure problemeffectively. Some node failure may leads to the breakdown of the whole system. The mostimportant advantage of P2P is that the system can adapt to multi-node dynamic changes, andhas good flexibility and scalability. Therefore, this paper proposes a GriP model, combined Gridwith P2P. By merging Grid and P2P, we can not only remedy defects of node structureinflexibility and node instability respectively, but also bring their respective advantages of nodecontrolling into full play. This model draws advantages of Grid and P2P and gets rid of theirinherent disadvantages.II. Presenting "Star-Ring-Mixed" P2P topology structure P-SRing which adapts to single-node failure and related problems. This paper presents a "two-layered mix" network topology structure, in which resourcenodes distribute using ring topology at lower layer, while every resource node connects proxynode using star topology at higher layer. At the ring layer, every resource node takes itsneighbor node in clockwise direction as successor node; when some node breaks down, doublerouting mechanism make the whole model more safe and stable. Once some node breaks down,its successor node will connect to related nodes automatically, and the whole network can stillrun normally.Ⅲ. Proposing an algorithm on how to rapidly locate the node in GriP based on resourcelocation and users' interests.There are two steps for searching for resources in Grid, which are rapidly locating resourcenodes and searching for resource contents. Concerning the first step, this paper proposesresource node search algorithm based on resource location and node feedback. This algorithmgroups the nodes according to location information, saves historical search feedback result,computes neighbor node's estimated degree of successful searching and dynamically forwardsinformation to physically connected node so as to save bandwidth and shorten time of findingthe first file. By computing neighbor node's interest correlation degree and adjusting neighbornodes periodically according to geographical position, the resource node search algorithmimproves stability and efficiency in resources searching.Ⅳ. Introducing a resource searching method in GriP based on Metadata and Balance Tree.As for resource content searching, in order to improve search efficiency in GriP, westudied effective search method, which is used to index metadata of resource. GriP appliesXML-based metadata to indexing method, and further puts forward a keyword-based resourcesearch method–QBVT. This method improves the tree structure, which adds parent node'sinformation into child nodes, and constructs a balance tree so as to reduce cost of tree updatingand improve search efficiency based on resource contents.
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