Content Locating in Distributed Social-Based Unstructured Peer-to-Peer Networks: An Interest Adaptive Approach
Date Issued
2009
Date
2009
Author(s)
Chang, Chih-Bang
Abstract
Content location is one of the most important problems in peer-to-peer networks. In this thesis, we discuss content location in a special peer-to-peer network, the social-based peer-to-peer networks. There are some researches which show that locating content is faster and easier in social-based peer-to-peer networks. We discover a new problem in social-based peer-to-peer networks, peers change their tasty. While peers change their tasty, the knowledge they collected is not useful as past. Hence, we proposed a decentralized interest adaptive approach to solve it, an interest adaptive content locating (IACL). It makes the two characters of social-based “clustered” and “recommendation” more wisely; it adapts the behavior that peers change their tasty. We also do some simulation to show our approach is better than other content locating methods on some experiment indices in peers change their tasty environment. From the simulation results, we know that the IACL method we proposed has enough success rate to locate content and lower messages overhead while query.
Subjects
content location
social network
peer-to-peer network
distributed network
Type
thesis
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