Enhancing Search Performance in Unstructured P2P Networks Based on Users’ Common Interest : Abstract

Abstract :

Peer-to-peer (P2P) networks establish loosely coupled application-level overlays on top of the Internet to facilitate efficient sharing of resources. They can be roughly classified as either structured or unstructured networks. Without stringent constraints over the network topology, unstructured P2P networks can be constructed very efficiently and are therefore considered suitable to the Internet environment. However, the random search strategies adopted by these networks usually perform poorly with a large network size. In this paper, we seek to enhance the search performance in unstructured P2P networks through exploiting users’ common interest patterns captured within a probability-theoretic framework termed the user interest model (UIM). A search protocol and a routing table updating protocol are further proposed in order to expedite the search process through self organizing the P2P network into a small world. Both theoretical and experimental analyses are conducted and demonstrated the effectiveness and efficiency of our approach.

Existing System:
Network system classified into two categories. They are structured and unstructured. Unstructured P2P networks can be constructed very efficiently.
The applications share the resources and data in this system is a challenging task. This challenge overcome by random search strategy.

Problem Definition
Random search strategy satisfies the problem of sharing data and resources in unstructured networks. It works properly for small networks. When we were going large networks, this system performance is very poor.

Proposed System:
We seek to enhance the search performance in unstructured P2P networks. The statistical patterns over locally shared resources of a peer can be explored to guide the distributed resource discovery process and therefore enhance the overall resource discovery performance in unstructured P2P networks. The following steps are used for this process.
I. Store the nodes in order to save peers from their blindness.
II. Users’ common interest patterns captured within a probability-theoretic framework termed the user interest model (UIM).
III. We are able to estimate the probability of any peer sharing a certain resource through UIM.
IV. When a peer receives a query for a certain file that is not available locally, it will forward the query to one of its neighbors that has the highest probability of actually sharing that file.
V. Updating the routing table protocol to support our search protocol through self organizing the whole P2P network.

S/W Specifications
Operating System : Windows XP
Front end : JAVA
Back end : SQLITE

H/W Specification
System Processor : Pentium IV
Processor Speed : 2.80GHz