适应自组织管理模式的P2P资源共享网络激励模型的研究

P2P网络中的自组织管理模式使得节点的自私行为大量存在.建立相应解决方案所面临的主要问题是难以继续维持P2P网络拥有的自组织及规模可扩放性等特性.目前存在的信用管理模型或者激励模型多是采用泛洪方式获得网络其他节点的历史行为信息,庞大的报文通信量和推导算法的高时间复杂度制约了所能应用的P2P网络的规模.本文提出了一种利用随机相遇博弈理论指导建立的P2P资源共享网络激励模型ResP2P.该模型通过引入节点信誉及信誉恢复区分机制,同时制定相关行为社会规范,来激励理性节点为使其自身收益最大化而向整个网络贡献资源,并且ResP2P所对应的分布式算法易于在自组织管理模式的网络环境中实施.实验证实了ResP2P模型激励机制的有效性.

An Incentive Model Adapted to Self-Organizing Management Mode for Resource Sharing in P2P Networks

The roll-organizing management mode in P2P networks leads to a large amount of selfish behaviors among peers. The corresponding ,solutions to this problem could hardly keep the merits of P2P network simultaneously, such as self-organization or dynamic scalabihty. Most of the proposed reputation management models or incentive ones use the flooding mechanism to learn historical behaviour information of other peers, which canses excessive incurred packets, and thus limits the dynamic scalability. A novel incentive framework named as ResP2P based on random matching games theory is given in the paper. Peer reputation and its renewal mechanism, along with some essential social norms are introduced in ResP2P model, which stimulates rational peers to maximize their own utility and contribute their free resource. Experiments have verified the validity and efficiency of the incentive mechanism. The relative distributed algorithm can easily be deployed in a P2P networks and satisfied with self-organization and scalability.