基于多层Bayesian信任网的P2P负载均衡模型

针对基于信誉机制的P2P(Peer-to-Peer)网络中的负载不均衡现象,提出一种基于多层Bayesian信任网的P2P负载均衡模型,介绍了如何在Bayesian信任网中引入多种负载指标以及它们之间的各种关联,并且使用该结构预测P2P网络中各服务节点的负载,使整个网络达到负载均衡.对包含负载指标的多层Bayesian信任模型和动态负载模型进行了实验比较.实验结果表明:基于Bayesian信任网的P2P网络负载均衡模型运用节点的历史交互信息,结合Bayesian预测理论,影响资源请求节点选择服务节点,实现P2P负载的有效均衡,确保整个P2P网络资源利用的高效性、节点服务的可靠性以及整个网络的稳定性.

P2P load balance model based on multi-layer Bayesian trust network

A Peer to Peer(P2P) load balance model based on multi layer Bayesian trust network was designed to deal with the load balance problems in the trust based P2P network. Some load indexes and their relations were added to the model to predict the workload of the peers. Experiments were done for the presented model and the dynamic load model, respectively. The results show that, by exploiting the interactions history and the Bayesian prediction theories, the load balance model based on multi layer Bayesian trust network influences the peer selection process and can effectively balance the load of the P2P network, thereafter improving the resource utilization ratio, the quality and reliability of the peers services．