为了从根本上解决现有互联网存在的可扩展性、移动性和安全性等方面的问题,全新的未来互联网体系结构得到了广泛研究.其中,命名数据网络(named data networking,简称NDN)利用网内缓存和多路转发实现了基于层次化名字的高效数据传输,从...为了从根本上解决现有互联网存在的可扩展性、移动性和安全性等方面的问题,全新的未来互联网体系结构得到了广泛研究.其中,命名数据网络(named data networking,简称NDN)利用网内缓存和多路转发实现了基于层次化名字的高效数据传输,从根本上解决了现有互联网所面临的问题.内容的层次化名字具有数量庞大、结构复杂等特点,现有的基于IP的路由转发机制无法直接应用于NDN网络,需要有针对性地研究高效的层次化名字路由机制,保证海量网络内容的正常路由转发.路由聚合是缩减网络路由规模的主要措施.不同于现有的面向本地NDN路由表查表过程的优化,路由聚合需要全网协同处理,在不同网络节点上不断对聚合路由进行聚合.这对聚合路由标识和聚合路由可用性评估提出了诸多要求.为此,研究并提出了针对层次化名字路由的聚合机制,包括两个方面的工作:(1)构建了一种全新的计数布隆过滤器——堆叠布隆过滤器,该过滤器支持多过滤器合并,用于压缩表示被聚合路由名字;(2)给出了一种动态路由聚合机制,在保证NDN网络路由转发准确性的同时,缩小全网路由规模,最大程度地优化了路由转发效率.在真实网络拓扑上构建了仿真平台,经过实验验证,该路由聚合机制以可控的少量冗余转发为代价,有效地压缩了全网路由规模,提升了全网路由转发效率,保证了海量在线内容的高效路由转发,为NDN网络投入实际部署提供了前提.展开更多
This paper discusses efficient estimation for the additive hazards regression model when only bivariate current status data are available. Current status data occur in many fields including demographical studies and t...This paper discusses efficient estimation for the additive hazards regression model when only bivariate current status data are available. Current status data occur in many fields including demographical studies and tumorigenicity experiments （Keiding, 1991; Sun, 2006） and several approaches have been proposed for the additive hazards model with univariate current status data （Linet M., 1998; Martinussen and Scheike, 2002）. For bivariate data, in addition to facing the same problems as those with univariate data, one needs to deal with the association or correlation between two related failure time variables of interest. For this, we employ the copula model and an efficient estimation procedure is developed for inference. Simulation studies are performed to evaluate the proposed estimates and suggest that the approach works well in practical situations. An illustrative example is provided.展开更多
文摘为了从根本上解决现有互联网存在的可扩展性、移动性和安全性等方面的问题,全新的未来互联网体系结构得到了广泛研究.其中,命名数据网络(named data networking,简称NDN)利用网内缓存和多路转发实现了基于层次化名字的高效数据传输,从根本上解决了现有互联网所面临的问题.内容的层次化名字具有数量庞大、结构复杂等特点,现有的基于IP的路由转发机制无法直接应用于NDN网络,需要有针对性地研究高效的层次化名字路由机制,保证海量网络内容的正常路由转发.路由聚合是缩减网络路由规模的主要措施.不同于现有的面向本地NDN路由表查表过程的优化,路由聚合需要全网协同处理,在不同网络节点上不断对聚合路由进行聚合.这对聚合路由标识和聚合路由可用性评估提出了诸多要求.为此,研究并提出了针对层次化名字路由的聚合机制,包括两个方面的工作:(1)构建了一种全新的计数布隆过滤器——堆叠布隆过滤器,该过滤器支持多过滤器合并,用于压缩表示被聚合路由名字;(2)给出了一种动态路由聚合机制,在保证NDN网络路由转发准确性的同时,缩小全网路由规模,最大程度地优化了路由转发效率.在真实网络拓扑上构建了仿真平台,经过实验验证,该路由聚合机制以可控的少量冗余转发为代价,有效地压缩了全网路由规模,提升了全网路由转发效率,保证了海量在线内容的高效路由转发,为NDN网络投入实际部署提供了前提.
基金partly supported by National Natural Science Foundation of China (Grant No. 10971015, 11131002)Key Project of Chinese Ministry of Education (Grant No. 309007)the Fundamental Research Funds for the Central Universities
文摘This paper discusses efficient estimation for the additive hazards regression model when only bivariate current status data are available. Current status data occur in many fields including demographical studies and tumorigenicity experiments （Keiding, 1991; Sun, 2006） and several approaches have been proposed for the additive hazards model with univariate current status data （Linet M., 1998; Martinussen and Scheike, 2002）. For bivariate data, in addition to facing the same problems as those with univariate data, one needs to deal with the association or correlation between two related failure time variables of interest. For this, we employ the copula model and an efficient estimation procedure is developed for inference. Simulation studies are performed to evaluate the proposed estimates and suggest that the approach works well in practical situations. An illustrative example is provided.
