A multilayer topic-group based P2P network

Abstract

Considering that large and various contents are shared over thousands of peers across a P2P network, one challenging problem is to locate the target content in such a large and unorganized environment. Hence, a P2P system designed with the content classification and managed distribution is more efficient and reliable than the mass and unclassified ones. We propose a MLTG (multilayer topic-group) based P2P network. Two core concepts are integrated into the system structure: topic-group and hierarchic layer. Peers act as local peers, delegate peers and super-peers. These peers spread on different hierarchic layers and the system distributes the correlative resources among the same groups. Compared with the traditional P2P schemes, the MLTG is out-performed significantly. From the experiments, it saves up to 90% of query cost for the case of a single keyword query. When the query consists of topic and keywords, it saves up to 93% of cost. MLTG guarantees the enhancement of successful search and lower consumption of bandwidth. It also makes the system more available, reliable and efficient.