State-Based Search Strategy in Unstructured P2P

Abstract

Efficient resource search in large-scale unstructured peer-to-peer (P2P) systems remains a fundamental challenge. In order to improve search performance, interest-based search is a good way to tackle the challenge. However, the existing interest-based search algorithms pay attention to user interest model and search history, but ignore some influence factors of improving performance. In this paper, through analysis of node's multi-dimensional quality-of-service (QoS) for search, it is found that node state's information is essential for improving search performance. So a State-Based Search (SBS) strategy is presented by improving Sripanidkulchai's interestbased search model, where Node's state evaluation is based on node's QoS fuzzification. The SBS enhances the methods of shortcuts adding, ranking and selecting, and state's life-cycle is predicted by grey theory. The experimental results show that the SBS can improve performance by reducing search response time and achieving load balance.

Enhanced Entropy‐Based Resource Searching in Unstructured P2P Networks

“…In order to simplify the complexity of semantic computing, some alternative schemes in Refs. [14][15][16] are proposed to build the shortcut links between peers with similar interests or preferences to improve search performance. However, this approach needs to cluster the semantic resources of peers only satisfying some particular searching requests, and leaves the logical links or routing state information out of consideration.…”