This work applies non-stationary random processes to resilience of power distribution under severe weather. Power distribution, the edge of the energy infrastructure, is susceptible to external hazards from severe wea...This work applies non-stationary random processes to resilience of power distribution under severe weather. Power distribution, the edge of the energy infrastructure, is susceptible to external hazards from severe weather. Large-scale power failures often occur, resulting in millions of people without electricity for days. However, the problem of large-scale power failure, recovery and resilience has not been formulated rigorously nor studied systematically. This work studies the resilience of power distribution from three aspects. First, we derive non-stationary random processes to model large-scale failures and recoveries. Transient Little’s Law then provides a simple approximation of the entire life cycle of failure and recovery through a queue at the network-level. Second, we define time-varying resilience based on the non-stationary model. The resilience metric characterizes the ability of power distribution to remain operational and recover rapidly upon failures. Third, we apply the non-stationary model and the resilience metric to large-scale power failures caused by Hurricane Ike. We use the real data from the electric grid to learn time-varying model parameters and the resilience metric. Our results show non-stationary evolution of failure rates and recovery times, and how the network resilience deviates from that of normal operation during the hurricane.展开更多
随着计算机网络的快速发展,其对于服务质量(Quality of Service)的需求和要求也越来越高。近年来,软件定义网络(Software Defined Network)逐渐兴起,由于软件定义网络的服务质量方法可编程性、可扩展性强,因而能够弥补传统服务质量的很...随着计算机网络的快速发展,其对于服务质量(Quality of Service)的需求和要求也越来越高。近年来,软件定义网络(Software Defined Network)逐渐兴起,由于软件定义网络的服务质量方法可编程性、可扩展性强,因而能够弥补传统服务质量的很多问题。提出了一种软件定义网络实现的动态服务质量方法,在转发队列和路由选择环节都能根据当前网络的状况进行决策;与此同时还会根据网络状态的变化以及数据包到达目的地的延时和丢包率情况来对决策进行调整和补偿。展开更多
文摘This work applies non-stationary random processes to resilience of power distribution under severe weather. Power distribution, the edge of the energy infrastructure, is susceptible to external hazards from severe weather. Large-scale power failures often occur, resulting in millions of people without electricity for days. However, the problem of large-scale power failure, recovery and resilience has not been formulated rigorously nor studied systematically. This work studies the resilience of power distribution from three aspects. First, we derive non-stationary random processes to model large-scale failures and recoveries. Transient Little’s Law then provides a simple approximation of the entire life cycle of failure and recovery through a queue at the network-level. Second, we define time-varying resilience based on the non-stationary model. The resilience metric characterizes the ability of power distribution to remain operational and recover rapidly upon failures. Third, we apply the non-stationary model and the resilience metric to large-scale power failures caused by Hurricane Ike. We use the real data from the electric grid to learn time-varying model parameters and the resilience metric. Our results show non-stationary evolution of failure rates and recovery times, and how the network resilience deviates from that of normal operation during the hurricane.
文摘随着计算机网络的快速发展,其对于服务质量(Quality of Service)的需求和要求也越来越高。近年来,软件定义网络(Software Defined Network)逐渐兴起,由于软件定义网络的服务质量方法可编程性、可扩展性强,因而能够弥补传统服务质量的很多问题。提出了一种软件定义网络实现的动态服务质量方法,在转发队列和路由选择环节都能根据当前网络的状况进行决策;与此同时还会根据网络状态的变化以及数据包到达目的地的延时和丢包率情况来对决策进行调整和补偿。
