在大规模数据中心网络中,链路故障检测是保障网络连通性,确保线上业务正常运转的重要手段.当前链路故障检测功能一般由中间盒设备来提供或被直接整合到交换设备中.随着软件定义网络和网络功能虚拟化(network function virtualization,N...在大规模数据中心网络中,链路故障检测是保障网络连通性,确保线上业务正常运转的重要手段.当前链路故障检测功能一般由中间盒设备来提供或被直接整合到交换设备中.随着软件定义网络和网络功能虚拟化(network function virtualization,NFV)技术的发展,各项网络功能正逐渐从专用设备中分离出来,以服务的形式部署在云端为用户提供解决方案.然而,当前链路故障检测方法面临着单次探测用时过长、网络带宽占用率过高以及服务器负载过重等严峻挑战,并不适用于构建实时性需求较高的云服务.为此,需要对已有链路故障检测工作中存在的问题进行分析,提出探测矩阵的概念,以及基于探测矩阵优化的链路故障检测方法,并设计一个链路故障检测控制器与SDN控制器协同的服务架构,以此实现云端的链路故障实时检测即服务.最后,通过仿真实验的方式验证了该实时检测方法在单次探测用时、网络带宽占用以及端点负载3方面同之前工作相比具有显著优势,且优化探测矩阵所带来的开销是可容忍的.展开更多
In this paper,a novel train positioning method considering satellite raw observation data was proposed,which aims to promote train positioning performance from an innovative perspective of the train satellite-based po...In this paper,a novel train positioning method considering satellite raw observation data was proposed,which aims to promote train positioning performance from an innovative perspective of the train satellite-based positioning error sources.The method focused on overcoming the abnormal observations in satellite observation data caused by railway environment rather than the positioning results.Specifically,the relative positioning experimental platform was built and the zero-baseline method was firstly employed to evaluate the carrier phase data quality,and then,GNSS combined observation models were adopted to construct the detection values,which were applied to judge abnormal-data through the dual-frequency observations.Further,ambiguity fixing optimization was investigated based on observation data selection in partly-blocked environments.The results show that the proposed method can effectively detect and address abnormal observations and improve positioning stability.Cycle slips and gross errors can be detected and identified based on dual-frequency global navigation satellite system data.After adopting the data selection strategy,the ambiguity fixing percentage was improved by 29.2%,and the standard deviation in the East,North,and Up components was enhanced by 12.7%,7.4%,and 12.5%,respectively.The proposed method can provide references for train positioning performance optimization in railway environments from the perspective of positioning error sources.展开更多
文摘在大规模数据中心网络中,链路故障检测是保障网络连通性,确保线上业务正常运转的重要手段.当前链路故障检测功能一般由中间盒设备来提供或被直接整合到交换设备中.随着软件定义网络和网络功能虚拟化(network function virtualization,NFV)技术的发展,各项网络功能正逐渐从专用设备中分离出来,以服务的形式部署在云端为用户提供解决方案.然而,当前链路故障检测方法面临着单次探测用时过长、网络带宽占用率过高以及服务器负载过重等严峻挑战,并不适用于构建实时性需求较高的云服务.为此,需要对已有链路故障检测工作中存在的问题进行分析,提出探测矩阵的概念,以及基于探测矩阵优化的链路故障检测方法,并设计一个链路故障检测控制器与SDN控制器协同的服务架构,以此实现云端的链路故障实时检测即服务.最后,通过仿真实验的方式验证了该实时检测方法在单次探测用时、网络带宽占用以及端点负载3方面同之前工作相比具有显著优势,且优化探测矩阵所带来的开销是可容忍的.
基金Project(52272339)supported by the National Natural Science Foundation of ChinaProject(2023YFB390730303)supported by the National Key Research and Development Program of China+2 种基金Project(L2023G004)supported by the Science and Technology Research and Development Program of China State Railway Group Co.,Ltd.Project(QZKFKT2023-005)supported by the State Key Laboratory of Heavy-duty and Express High-power Electric Locomotive,ChinaProject(2022JZZ05)supported by the Open Foundation of MOE Key Laboratory of Engineering Structures of Heavy Haul Railway(Central South University),China。
文摘In this paper,a novel train positioning method considering satellite raw observation data was proposed,which aims to promote train positioning performance from an innovative perspective of the train satellite-based positioning error sources.The method focused on overcoming the abnormal observations in satellite observation data caused by railway environment rather than the positioning results.Specifically,the relative positioning experimental platform was built and the zero-baseline method was firstly employed to evaluate the carrier phase data quality,and then,GNSS combined observation models were adopted to construct the detection values,which were applied to judge abnormal-data through the dual-frequency observations.Further,ambiguity fixing optimization was investigated based on observation data selection in partly-blocked environments.The results show that the proposed method can effectively detect and address abnormal observations and improve positioning stability.Cycle slips and gross errors can be detected and identified based on dual-frequency global navigation satellite system data.After adopting the data selection strategy,the ambiguity fixing percentage was improved by 29.2%,and the standard deviation in the East,North,and Up components was enhanced by 12.7%,7.4%,and 12.5%,respectively.The proposed method can provide references for train positioning performance optimization in railway environments from the perspective of positioning error sources.
