No abstract

Recently, there has been increasing interest in research on data sharing in peer-to-peer networks. In our previous work, we proposed a novel update propagation strategy that creates an n-ary tree, whose root is the owner of the original data while the other nodes are peers holding its replicas, and propagates the update information according to the tree. In this paper, we extend our previous strategy to further reduce the delay for update propagation and to tolerate peers' failure. To achieve this, in the extended strategy, peers participating in the tree record the information of their ancestors and children in the tree and reconstruct the tree using that information when some peers in the tree fail.

No abstract

In P2P networks, it is effective to allocate replicas of each data item to multiple peers for improving search efficiency and data availability. It has been mathematically proved that the square-root allocation, in which the ratios of numbers of replicas are proportional to the square-root of their access frequencies, is optimal in terms of search efficiency. In this paper, we propose a replica relocation method that not only nearly achieves the square-root allocation but also distributes replicas uniformly in the network as much as possible. Our method creates replicas at each peer on the path along which a query is successfully forwarded. Here, each peer on the path determines whether it creates the replica or not based on the access frequency of the data item. In addition, for creating a new replica when a cache memory space is full, our method preferentially deletes a replica of a data item which has been replicated at a large number of peers.

To improve the efficiency of information retrieval in P2P networks, there have been many researches on categorizing data items and clustering peers. In almost all these researches, the number of categories and the policy of categorization are predetermined and static. However, users' requirements for information retrieval dynamically change. This leads to undesired increase of network traffic. In this paper, we propose a dynamic cluster construction method based on query characteristics and a search method using dynamic cluster. Our method dynamically constructs clusters, when the access frequencies for certain data items increase. This approach can reduce the number of query messages for searching data items further than static clustering methods.