Adaptive Resource Indexing Technique for Unstructured Peer-to-Peer Networks

Searching for particular resources in a large-scale decentralized unstructured network can be very difficult since there is no centralized management to provide the specific location of resources. Moreover, the dynamic behavior of networks and the diversity of user behavior cause the search more complex and may not guarantee success. To address the problems, we propose a new adaptive resource indexing technique that aims to increase both efficiency and quality of the search by reducing both messages and time required for each query. Our approach consists of two complementary techniques. One is an index selection technique that selectively keeps the indices at each peer to increase the chance of successful queries with minimum space requirement. Another is an index distribution technique that automatically adjusts index distribution rate based on the search performance to optimize both the search performance and overhead. We simulate the technique in various network conditions and the results show that our technique is effective in decreasing hop counts and messages needed for resolving queries with only small overhead. It decreases the average hop count by up to 44% with 75%-less messages when used with flooding based queries even facing high churn. Furthermore, the query success rate with a limited timeout condition also increases, approaching nearly to 100%.