Abstract-Content management appears as an essential requirement in order to deploy enhanced P2P networks. In P2P networks, the content that is requested by the query node could be located at different locations/nodes; therefore an efficient search mechanism is required. The proposed search algorithm in this paper, called socP2P, relies on peers' social relationships (friendships, shared interests, shared background and experiences) to improve the content discovery compared to similar solutions. With socP2P nodes can improve searches by using knowledge gained by 'overheard information' during their stay in the network. In addition, our algorithm exploits peers' common interests, friendships, and capability of memorizing experiences (received and routed queries) by them. Simulation results show that socP2P is able to achieve a high success rate, low delay and low overhead. We have verified that our algorithm is not only useful in finding popular contents in the network but also good enough to locate rare files. The obtained results reflect that exploiting social information in P2P networks leads to a more efficient content search mechanism. Keywords: Content Discovery, Peer-to-Peer networks, Social Network, Random Walk I. BACKGROUND AND RELATED WORK A Peer-to-Peer (P2P) network is a virtual overlay network, built at application layer, on top of a physical network topology. Decentralization and self-organization are key features of P2P networks. AS the number and location of contents in a P2P system have a strong impact on the performance, it is not surprising that content management in P2P networks is one of the research challenges in this area. Peers do not have global knowledge about the network and they have to interact with each other to find the required resource. With the increasing number of peers, the rate of possible interactions may also increase exponentially and hence this strategy is not applicable. In addition, existing unstructured P2P search techniques create huge network traffic or they require high search overhead [4] . Most of the search methods for unstructured P2P networks are focusing on solving their issues by means of blind flooding. Authors in [5] present a new social-like P2P algorithm (Social-P2P) for resource discovery by mimicking human interactions in social networks where peer nodes are people and connections are relationships. Random Walk [6] is one popular searching method in P2P which a query is forwarded to a randomly chosen neighbor hop by hop until enough responses have been found. Also Gnutella [8] appears as one of the most popular P2P file-sharing applications which require nodes to broadcast messages to their neighbors in order to perform content lookup. Given that recent studies of P2P systems have provided a series of useful solutions to improve the performance of content management in P2P networks, realizing an advanced method of content management by means of social relations of users in OSNs is still an open issue in ongoing research. The objectives, requirements and challenges for P2P social networks have been formulated in [1] . In this paper, we use interest similarity gestures (exhibited by the fact that both nodes are interested in the same content) to establish P2P links rather than relying on random associations or DHT (Distributed Hash Table) rules. The proposed algorithm, named socP2P, uses social relation of peers to improve content management. Finally, we evaluate the performance of the proposed algorithm via simulation in terms of average success rate and path length to desired contents. In addition, we compare socP2P with Random walk algorithm. The rest of this paper is organized as follows. The proposed resource search algorithm, socP2P, is presented in section II. Section III contains the performance evaluation of the proposed algorithm. Finally, we conclude the paper and present future direction in Section IV. II. SOCP2P, PROPOSED RESOURCE SEARCH ALGORITHM As stated earlier, the driving concept in this paper is to extract the social characteristics of the human beings behind a network of peers in a P2P system, and use them to improve the content management. We want to identify nodes with similar interests and enable them to help each other to find desired content. Nodes do not need to declare their interests, because inferring the interest of other nodes in the network is a gradual process for all nodes in the network. The more time they spend in the network the more they learn. Hence, they can route queries towards the right nodes more efficiently. In addition, peers may develop friendship with nodes having similar interest, so peers can generate their friends list and use it to route queries in the network. In our design, we create new links between peers if we find/detect an interest similarity between them. For example, when node 'A' makes a request for object 'X', and if it gets a successful reply from node B, both nodes learn that they have a matching interest. Our approach differs in detecting interests between nodes compared to [2] that use the concept of providing a strength value to each node in a friend list. Furthermore, a node that generates a query and the node that successfully responds to that query are considered to share similar interest. Thus each interaction between nodes helps to determine if two nodes have interest similarity or not. In addition, each node has the capability of memorizing those received queries even if they are not successful. Therefore, it is also able to recommend a "right node" (i.e. a node that likely stores the searched content). We have tried to use interest similarity gestures to make P2P links rather than relying on random associations or DHT rules. The links are created in two ways based on interest similarity. In the first method, Directed Interest Links (DIL) is created based on interest similarity between nodes. For simplicity, we set that after nodes A and B learn that they have a matching interest, node A creates an Interest Link with node B. We avoid creating a bidirectional link here, because as it happens in our normal life, relationship links may not always be mutual. For example as OSNs have shown us, some people are very well known and popular in the society, so many people like to create a link with them. However, these kinds of links are not often bidirectional. The second type of link creation is the Network Suggested Forwards (NSF) and then Created Links model. The above association is the simplest and it is quite limited because it does not exploit the fact that there may be other copies of object X in the network, which node A and node B may not be aware of. The network is usually flooded with queries directed to other nodes for searching purposes. A node may receive queries for the same content more than once. In this case, the node that receives the query has already learned or memorized that some other nodes were looking for the same content earlier and instead of flooding the query in the network or forwarding the query in a random way, the query node can be recommended to get the content directly from the node that was looking for the same content earlier. Moreover, it is now possible to learn that the two nodes share interest profiles and so there is a possibility for them to create an interest-link which otherwise would have not been discovered. A network node keeps records and uses search queries to automatically adapt and make note of objects that indicate related interest relationships. Fig. 1 shows an NSF model example in which node A is looking for content X. Node A sends query to node E, which memorizes the received query and since node E does not have the content X, it forwards the query to node C. Node C has the content X, thus it sends back the requested content to querying node A. Later in the time, node B looks for the same content X. Then, it sends its query to node E, which has previously encountered the same query. Thus, it recommends node A to node B assuming that node A might have the content X as it was looking for it earlier. Node E also forwards node B's query to node D. Since node D has the content X, it gives back the desired content to node B. The recommended node A also provides the content to node B. In this scenario, node A and node B are interested in same content X. Node C and node D already had the content X.
In REF a friend network is proposed on the basis of similarity of interests.
socP2P: P2P content discovery enhancement by considering social networks characteristics
2012 IEEE Symposium on Computers and Communications (ISCC)
2012 IEEE Symposium on Computers and Communications (ISCC)
Computer Science