Peer-to-peer (P2P) overlay networks are widely employed in distributed systems. The number of hops required by a node to locate an object is the fundamental search cost of a P2P network. Creating replicas can efficiently reduce the cost of object search, so how to deploy replicas to reduce the cost as much as possible is a critical problem of P2P networks. In the literature, most existing replica placement strategies arrange replicas at nodes near the one containing the considered object. In this paper, we formally demonstrate that for a complete Chord P2P network and many non-complete Chord ones, due to their deterministic structures, we can allocate replicas to nodes closest to the target in the identifier space to maximize the reduction in the total number of hops required by all nodes to reach a copy of the object during the search heading to the target node.

In P2P applications, networks are formed by devices belonging to independent users. Therefore, routing hotspots or routing congestions are typically created by an unanticipated new event that triggers an unanticipated surge of users to request streaming service from some particular nodes; and a challenging problem is how to provide incentive mechanisms to allocation bandwidth more fairly in order to avoid congestion and other short backs for P2P QoS. In this paper, we study P2P bandwidth game — the bandwidth allocation in P2P networks. Unlike previous works which focus either on routing or on forwarding, this paper investigates the game theoretic mechanism to incentivize node's real bandwidth demands and propose novel method that avoid congestion proactively, that is, prior to a congestion event. More specifically, we define an incentive-compatible pricing vector explicitly and give theoretical proofs to demonstrate that our mechanism can provide incentives for nodes to tell the true bandwidth demand. In order to apply this mechanism to the P2P distribution applications, we evaluate our mechanism by NS-2 simulations. The simulation results show that the incentive pricing mechanism can distribute the bandwidth fairly and effectively and can also avoid the routing hotspot and congestion effectively.

This letter analyzes a resource chain trust model for P2P reputation-based systems. Many researchers have given a lot of efforts to reputation-based system area and some of them have made good theoretical models. Problems are to spread malicious contents whereas the remark that such models only concentrate on the relationship between the node and its direct neighbors is still controversial. To solve the problems, we introduced the RCM (Resource Chain Model) and the Enhanced RCM. In this letter, we analyze the models and then show usage of our models can help us to find the best and safest location efficiently and decrease the number of malicious transaction.

P2P (peer-to-peer) file sharing systems have been in operation for years. However, recent studies show that many peers in P2P networks are free-riders, who download files but are unwilling to share. This paper proposes a randomized search algorithm that considers the potential of a peer for information exchange and controlled query forwarding in the search process. Based on churn situations, the simulation results in this study demonstrate that the proposed algorithm can reduce network traffic and search latency while searching for files in the system.

Using Bloom filters is one of the most popular and efficient lookup methods in P2P networks. A Bloom filter is a representation of data item indices, which achieves small memory requirement by allowing one-sided errors (false positive). In the lookup scheme besed on the Bloom filter, each peer disseminates a Bloom filter representing indices of the data items it owns in advance. Using the information of disseminated Bloom filters as a clue, each query can find a short path to its destination. In this paper, we propose an efficient extension of the Bloom filter, called a Deterministic Decay Bloom Filter (DDBF) and an index dissemination method based on it. While the index dissemination based on a standard Bloom filter suffers performance degradation by containing information of too many data items when its dissemination radius is large, the DDBF can circumvent such degradation by limiting information according to the distance between the filter holder and the items holders, i.e., a DDBF contains less information for faraway items and more information for nearby items. Interestingly, the construction of DDBFs requires no extra cost above that of standard filters. We also show by simulation that our method can achieve better lookup performance than existing ones.

The overall performance of P2P-based file sharing applications is becoming increasingly important. Based on the Adaptive Resource-based Probabilistic Search algorithm (ARPS), which was previously proposed by the authors, a novel probabilistic search algorithm with QoS guarantees is proposed in this letter. The algorithm relies on generating functions to satisfy the user's constraints and to exploit the power-law distribution in the node degree. Simulation results demonstrate that it performs well under various P2P scenarios. The proposed algorithm provides guarantees on the search performance perceived by the user while minimizing the search cost. Furthermore, it allows different QoS levels, resulting in greater flexibility and scalability.

A novel Adaptive Resource-based Probabilistic Search algorithm (ARPS) for P2P networks is proposed in this paper. ARPS introduces probabilistic forwarding for query messages according to the popularity of the resource being searched. A mechanism is introduced to estimate the popularity and adjust the forwarding probability accordingly such that a tradeoff between search performance and cost can be made. Using computer simulations, we compare the performance of ARPS with several other search algorithms. It is shown that ARPS performs well under various P2P scenarios. ARPS guarantees a success rate above a certain level under all circumstances, and enjoys high and popularity-invariant search success rate. Furthermore, ARPS adapts well to the variation of popularity, resulting in high efficiency and flexibility.

In peer-to-peer (P2P) networks, reputation is used to estimate the trustworthiness of servents and to help prevent untrustworthy resources from spreading by malicious servents. However, in dynamic scenarios with arrivals and departures of servents and resources, servent reputation is not enough to reduce the impacts of malicious behaviors such as lying, whitewashing, etc. In this paper, we propose a new reputation management model using both servent and resource reputation and demonstrate detail protocols to implement our model in structured P2P networks. Simulation results show that our model can reduce the rate of downloading untrustworthy resources more rapidly than the previous models even in dynamic scenarios where servents can rejoin with new identities, introduce new untrustworthy resources, and send wrong feedbacks. Also, we show that the proposed model and protocol can effectively share the load between servents.

Content-Addressable Network (CAN) provides a mechanism that could retrieve objects in a P2P network by maintaining indices to those objects in a fully decentralized manner. In the CAN system, index caching is a useful technique for reducing the response time of retrieving objects. The key points of effective caching techniques are to improve cache hit ratio by actively sharing caches distributed over the P2P network with every node and to reduce a maintenance and/or routing overhead for locating the cache of a requested index. In this paper, we propose a new caching technique based on the notion of proxy-type caching techniques which have been widely used in WWW systems. It can achieve active cache sharing by incorporating the concept of proxy caching into the index access mechanism and locate a closer proxy cache of a requested index with a little routing overhead. By the result of simulations, we conclude that it can improve the response time of retrieving indices by 30% compared with conventional caching techniques.