近年来，网络数据呈爆炸性增长态势，云模式下的数据中心及P2P网络的集成架构成为研究热点。本项目致力于融合云数据中心与P2P网络的资源部署、副本管理及纳什均衡等问题的策略研究、模型建立及系统优化。针对数据中心层提出基于完全/智能polling的资源部署策略；针对P2P层提出副本立即/延迟修复策略及节点休眠策略；针对用户请求层提出综合考虑传输收益和等待成本的纳什均衡策略。建立请求队列数可变的空竭/非空竭服务统计轮询随机模型，进行数据中心层资源部署方案的评估与优化。针对P2P层的副本修复及节点休眠策略，建立服务台数可变的休假随机模型，合理设置副本修复阈值及休假长度等参数，优化P2P层网络服务。结合随机模型理论与博弈论，进行博弈均衡分析，研究服务节点的激励机制及请求节点的收费机制，实现系统的社会最优。项目研究成果既可以优化集成架构的性能，又可以扩展随机理论的研究范畴，具有重要的理论和应用价值。

In recent years, data in computer networks has exploded, integrated architecture of data center and P2P network based on cloud mode becomes the hot topic of research. In this context, we devote to the issues of strategy design, model building and system optimization in network resource management, including resource deployment, duplicate management and Nash equilibrium. Resource deployment strategies with fully polling and intelligent polling are proposed in data center layer. For P2P layer, immediate and delayed duplicate repair strategies, as well as energy conservation strategies are given. By taking into account the transmission reward and the waiting cost in user request layer, Nash equilibrium strategies are presented. In order to estimate and optimize the resource deployment strategy in data center layer, exhaustive and non-exhaustive statistically polling stochastic models with random queues are built. According to the working principles of duplicate repair and node energy conservation, stochastic models with vacation and random servers are established, then the duplicate repair threshold and sleeping length are set optimally to improve the service quality of P2P networks. Combining stochastic model theory and game theory, the game equilibrium strategy is analyzed, the incentive mechanism for the serving nodes and the pricing policy for the request nodes are studied, then the system is optimized socially. The achievement of this project can be used to optimize the system performance of the integrated framework, as well as extend the research scope of stochastic model. It is of importance in theory and practice.