基于兴趣层次的RTI拥塞控制研究

Study on RTI Congestion Control Based on the Layer of Interest

大规模分布式仿真造成数据的爆炸性增长,极大地限制了仿真的规模化和持续性.因为高层体系结构HLA(high level architecture)体系本身固有的缺乏对数据要求级别的定义,RTI(runtime infrastructure)不能充分利用仿真应用的特点进行拥塞控制.兴趣层次描述了不同的接收者对数据的需求差异,提供了一种引入QoS(quality of service)、拥塞控制和层次化数据分发管理DDM(data distribution management)等技术的基础.将兴趣层次理论应用到RTI的运行机制,提出了一种基于兴趣层次的相位过滤算法以进行运行时支撑环境RTI的拥塞控制.实验表明,基于兴趣层次的相位过滤算法对于控制RTI拥塞状况是有效的,同时具有关键数据和传输稳定性的保障.

The data explosion in large-scale distributed simulations cripples the performance of simulation and restricts the scalability and persistence. Because HLA (high level architecture) has inherent limitations to define the level of demands for data, RTI (runtime infrastructure) cannot utilize the simulation features effectively to make congestion control. The LOI (layer of interest) depicts the differences of demands for data in various receivers and provides a way to make some encouraging technologies feasible, such as QoS (quality of service), congestion control and layered DDM (data distribution management) strategy. In this paper, the LOI is combined into RTI's working mechanisms and a phase filtering based on the LOI is put forward to control congestions in RTI. Finally, experimental results are presented to demonstrate the efficient control over congestions in RTI using the LOI-based phase filtering, together with the critical data and transportation stability guarantee.