In this paper, we propose a new dynamic load balancing scheme according to load threshold adjustment and incentives mechanism. The proposed scheme adjusts the load threshold of a node by comparing it with a mean threshold of adjacent nodes, thereby increasing the threshold evenly. We also assign the incentives and penalties to each node through a comparison of the mean threshold of all the nodes in order to increase autonomous load balancing participation.

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.

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.