Gossip-Based Resource Location Strategy in Interest Community for P2P Networks

来源 :Chinese Journal of Electronics | 被引量 : 0次 | 上传用户:cczzss1122334455
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Resource location strategy has become very popular for mass data distribution in complex and dynamic network. Searching for the objects is a fundamental problem to unstructured Peer to peer(P2P) networks. In this paper, we propose a resource location strategy for user requirements, which employs the collaborative exchange of information between peers to construct interest communities. This strategy can gather similar peers and disseminate useful information among them. Furthermore, we design an algorithm in unstructured P2 P systems named Appropriate degree gossip search algorithm(ADGSA). Using this search algorithm, the performance including search success ratio, recall rate and search response time has been improved dramatically. An efficient resource location system with a fast organization of users cluster based on their requirements can be provided, preventing the creation of unliked communities. The simulation results show that our strategy has better search efficiency, short response time and high recall ratio. Resource location strategy has become very popular for mass data distribution in complex and dynamic network. Searching for the objects is a fundamental problem to unstructured Peer to peer (P2P) networks. In this paper, we propose a resource location strategy for user requirements, which employs the collaborative exchange of information between peers to construct interest in communities. This, we can design similar algorithm and disseminate useful information among them. Also, we design an algorithm in unstructured P2 P systems named Appropriate degree gossip search algorithm (ADGSA). Using this search algorithm, the performance including search success ratio, recall rate and search response time has been improved dramatically. An efficient resource location system with a fast organization of users cluster based on their requirements can be provided, preventing the creation of unliked communities. show that our strategy has better search efficiency, short res ponse time and high recall ratio.
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