A PROXIMITY-AWARE INTEREST-CLUSTERED P2P FILE SHARING SYSTEM

ABSTRACT:

Efficient file query is important to the overall performance of peer-to-peer (P2P) file sharing systems. Clustering peers by their common interests can significantly enhance the efficiency of file query. Clustering peers by their physical proximity can also improve file query performance. However, few current works are able to cluster peers based on both peer interest and physical proximity. Although structured P2Ps provide higher file query efficiency than unstructured P2Ps, it is difficult to realize it due to their strictly defined topologies. In this work, we introduce a Proximity-Aware and Interest-clustered P2P file sharing System (PAIS) based on a structured P2P, which forms physically-close nodes into a cluster and further groups physically-close and common-interest nodes into a sub cluster based on a hierarchical topology. PAIS uses an intelligent file replication algorithm to further enhance file query efficiency. It creates replicas of files that are frequently requested by a group of physically close nodes in their location. Moreover, PAIS enhances the intra-sub-cluster file searching through several approaches. First, it further classifies the interest of a sub-cluster to a number of sub-interests, and clusters common-sub-interest nodes into a group for file sharing. Second, PAIS builds an overlay for each group that connects lower capacity nodes to higher capacity nodes for distributed file querying while avoiding node overload. Third, to reduce file searching delay, PAIS uses proactive file information collection so that a file requester can know if its requested file is in its nearby nodes. Fourth, to reduce the overhead of the file information collection, PAIS uses bloom filter based file information collection and corresponding distributed file searching. Fifth, to improve the file sharing efficiency, PAIS ranks the bloom filter results in order. Sixth, considering that a recently visited file tends to be visited again, the bloom filter based approach is enhanced by only checking the newly added bloom filter information to reduce file searching delay. Trace-driven experimental results from the real-world Planet Lab test bed demonstrate that PAIS dramatically reduces overhead and enhances the efficiency of file sharing with and without churn. Further, the experimental results show the high effectiveness of the intra-sub-cluster file searching approaches in improving file searching efficiency.

AIM

The aims of this paper PAIS uses an intelligent file replication algorithm to further enhance file query efficiency.

SCOPE

 The Scope of this project shows the high effectiveness of the intra-sub-cluster file searching approaches in improving file searching efficiency.

EXISTING SYSTEM

Another class of methods to improve file location efficiency is through a proximity-aware structure. A logical proximity abstraction derived from a P2P system does not necessarily match the physical proximity information in reality. The shortest path according to the routing protocol (i.e. the least hop count routing) is not necessarily the shortest physical path. This mismatch becomes a big obstacle for the deployment and performance optimization of P2P file sharing systems. A P2P system should utilize proximity information to reduce file query overhead and improve its efficiency. In other words, allocating or replicating a file to a node that is physically closer to a requester can significantly help the requester to retrieve the file efficiently. Proximity-aware clustering can be used to group physically close peers to effectively improve efficiency. The third class of methods to improve file location efficiency is to cluster nodes with similar interests, which reduce the file location latency.

DISADVANTAGES:

1.  It  is harder to realize it in structured P2P systems due to their strictly defined topologies
2.  They have high efficiency of file location than unstructured P2Ps.

PROPOSED SYSTEM

In this paper, introduce a proximity aware and interest-clustered P2P file sharing system (PAIS) based on a structured P2P. It groups peers based on both interest and proximity by taking advantage of a hierarchical structure of a structured P2P. PAIS uses an intelligent file replication algorithm that replicates a file frequently requested by physically close nodes near their physical location to enhance the file lookup efficiency. Finally, PAIS enhances the file searching efficiency among the proximity-close and common-interest nodes through a number of approaches. The trace-driven experimental results on Planet Lab demonstrate the efficiency of PAIS in comparison with other P2P file sharing systems. It dramatically reduces the overhead and yields significant improvements in file location efficiency even in node dynamism. Also, the experimental results show the effectiveness of the approaches for improving file searching efficiency among the proximity-close and common interest nodes

ADVANTAGES

1.  It dramatically reduces the overhead and yields significant improvements in file location efficiency even in node dynamism.
2.  PAIS enhances the file searching efficiency among the proximity-close and common-interest nodes through a number of approaches

SYSTEM ARCHITECTURE:

SYSTEM CONFIGURATION

HARDWARE REQUIREMENTS:-

·                 Processor   -   Pentium –III

·                Speed                -    1.1 Ghz

·                RAM                 -    256 MB(min)

·                Hard Disk         -   20 GB

·                Floppy Drive    -    1.44 MB

·                Key Board                 -    Standard Windows Keyboard

·                Mouse               -    Two or Three Button Mouse

·                Monitor             -    SVGA

SOFTWARE REQUIREMENTS:-

·                Operating System              : Windows  7                                       

·                Front End                  : JSP AND SERVLET

·                Database                  : MYSQL

·                Tool                           :NETBEANS

REFERENCE:

Guoxin Liu , Ward, L. Haiying Shen. “A Proximity-Aware Interest-Clustered P2p File Sharing System”, IEEE Transactions on Parallel and Distributed Systems, Volume 26, Issue 6 MAY 2014.