A Proximity-Aware Interest-Clustered P2P File Sharing System(2015)

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 PlanetLab testbed 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

EXISTING SYSTEM:

1.  A key criterion to judge a P2P file sharing system is its file location efficiency. To improve this efficiency, numerous methods have been proposed. One method uses a super peer topology which consists of supernodes with fast connections and regular nodes with slower connections. A supernode connects with other supernodes and some regular nodes, and a regular node connects with a supernode. In this super-peer topology, the nodes at the center of the network are faster and therefore produce a more reliable and stable backbone. This allows more messages to be routed than a slower backbone and, therefore, allows greater scalability. Super-peer networks occupy the middle-ground between centralized and entirely symmetric P2P networks, and have the potential to combine the benefits of both centralized and distributed searches.
2.  Another class of methods to improve file location efficiency is through a proximity-aware structure.
3.  The third class of methods to improve file location efficiency is to cluster nodes with similar interests which reduce the file location latency.

DISADVANTAGES OF EXISTING SYSTEM:

 Although numerous proximity-based and interest-based super-peer topologies have been proposed with different features, few methods are able to cluster peers according to both proximity and interest.

 In addition, most of these methods are on unstructured P2P systems that have no strict policy for topology construction.

 They cannot be directly applied to general DHTs in spite of their higher file location efficiency.

PROPOSED SYSTEM:

     This paper presents a proximity-aware and interest-clustered P2P file sharing System (PAIS) on a structured P2P system. It forms physically-close nodes into a cluster and further groups physically-close and common-interest nodes into a sub-cluster. It also places files with the same interests together and make them accessible through the DHT Lookup() routing function. More importantly, it keeps all advantages of DHTs over unstructured P2Ps. Relying on DHT lookup policy rather than broadcasting, the PAIS construction consumes much less cost in mapping nodes to clusters and mapping clusters to interest sub-clusters. PAIS uses an intelligent file replication algorithm to further enhance file lookup efficiency.

1.  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
2.  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.
3.  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.
4.  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.
5. v Fourth, to reduce the overhead of the file information collection, PAIS uses bloom filter based file information collection and corresponding distributed file searching.
6.  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.

ADVANTAGES OF PROPOSED SYSTEM:

1.  The techniques proposed in this paper can benefit many current applications such as content delivery networks, P2P video-on-demand systems, and data sharing in online social networks.
2.  We introduce the detailed design of PAIS. It is suitable for a file sharing system where files can be classified to a number of interests and each interest can be classified to a number of sub-interests.
3.  It groups peers based on both interest and proximity by taking advantage of a hierarchical structure of a structured P2P.
4.  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.
5.  PAIS enhances the file searching efficiency among the proximity-close and common interest nodes through a number of approaches.

MODULES:

•  PAIS Structure
•  Node proximity representation
•  Node interest representation
•  Clustering physically close and common-interest nodes
•  File Distribution

MODULES DESCSRIPTION:

PAIS Structure

PAIS is developed based on the Cycloid structured P2P network. A node’s interests are described by a set of attributes with a globally known string description such as “image” and “music”. The strategies that allow the description of the content in a peer with metadata can be used to derive the interests of each peer. Taking advantage of the hierarchical structure of Cycloid, PAIS gathers physically close nodes in one cluster and further groups nodes in each cluster into sub-clusters based on their interests.

Node proximity representation

A landmarking method can be used to represent node closeness on the network by indices used. Landmark clustering has been widely adopted to generate proximity information. It is based on the intuition that nodes close to each other are likely to have similar distances to a few selected landmark nodes. We assume there are m landmark nodes that are randomly scattered in the Internet. Each node measures its physical distances to the m landmarks and uses the vector of distances as its coordinate in Cartesian space. Two physically close nodes will have similar vectors. We use space-filling curves, such as the Hilbert curve, to map the m-dimensional landmark vectors to real numbers, so the closeness relationship among the nodes is preserved. We call this number the Hilbert number of the node denoted by H. The closeness of two nodes’ Hs indicates their physical closeness on the Internet.

Node interest representation

Consistent hash functions such as SHA-1 is widely used in DHT networks for node or file ID due to its collision-resistant nature. When using such a hash function, it is computationally infeasible to find two different messages that produce the same message digest. The consistent hash function is effective to cluster messages based on message difference.

Clustering physically close and common-interest nodes

Based on the Cycloid topology and ID determination, PAIS intelligently uses cubical indices to distinguish nodes in different physical locations and uses cyclic indices to further classify physically close nodes based on their interests. Specifically, PAIS uses node i’s Hilbert number, Hi, as its cubical index, and the consistent hash value of node i’s interest as its cyclic index to generate node i’s ID denoted. If a node has a number of interests, it generates a set of IDs with different cyclic indices. Using this ID determination method, the physically close nodes with the same H will be in a cluster, and nodes with similar H will be in close clusters in PAIS. Physically close nodes with the same interest have the same ID, and they further constitute a sub-cluster in a cluster.

File Distribution

As physically close and common-interest nodes form a subcluster, they can share files between each other so that a node can retrieve its requested file in its interest from a physically close node. For this purpose, the sub-cluster server maintains the index of all files in its sub-cluster for file sharing among nodes in its sub-cluster. A node’s requested file may not exist in its sub-cluster. To help nodes find files not existing in their sub-clusters, as in traditional DHT networks, PAIS re-distributes all files among nodes in the network for efficient global search.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

 System                          :         Pentium IV 2.4 GHz.

 Hard Disk                      :         40 GB.

 Floppy Drive                 :         1.44 Mb.

 Monitor                         :         15 VGA Colour.

 Mouse                            :         Logitech.

 Ram                               :         512 Mb.

SOFTWARE REQUIREMENTS:

 Operating system           :         Windows XP/7.

 Coding Language          :         JAVA/J2EE

 IDE                                    :         Netbeans 7.4

 Database                         :         MYSQL

REFERENCE:

Haiying Shen, Senior Member, IEEE, Guoxin Liu, Student Member, IEEE and Lee Ward, “A Proximity-Aware Interest-Clustered P2P File Sharing System”, IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 26, NO. 6, JUNE 2015.