Analytical methods for planning for, and recovery from, multiple network or system failures due to nature or sabotage

S. Leibholz
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引用次数: 1

Abstract

Planning for recovery from exogenous or endogenous failures or outages of complex networks, including communications networks, power stations and entire grids as well as physical-transport networks such as pipelines, trucking, airways and railroads-for the purpose of smartly executing recovery-present planning difficulties because of the combinations of events which can occur, whether random, weather-induced, cascading or the effects of well-planned terrorism. These are poorly modeled by simple “Monte Carlo” runs and probability calculations because multiple events are rarely statistically independent, especially when (1) smart terrorists are the protagonist, or (2) when cascade effects occur, as at Fukushima. We discuss a mathematical model based on this author's analogous communications network model, CAINS (Communications and Information Network Solver) for assisting in the forward and contemporaneous evaluation and planning of backups and responses to such events. Abjuring Monte-Carlo simulation for stated reasons, the model is analytical and statistical in nature, avoids the problems with Monte-Carlo run length and chaotic algorithms, and thus runs very rapidly when planning for large numbers of failure modes.
针对自然或破坏造成的多重网络或系统故障进行规划和恢复的分析方法
从外生或内生故障或复杂网络(包括通信网络、发电站和整个电网以及物理运输网络,如管道、卡车运输、航空和铁路)的中断中进行恢复计划,以明智地执行恢复,由于可能发生的事件的组合,无论是随机的、天气引起的、级联的还是精心策划的恐怖主义的影响,目前的计划困难。简单的“蒙特卡罗”运行和概率计算很难模拟这些事件,因为多个事件在统计上很少是独立的,尤其是当(1)聪明的恐怖分子是主角,或(2)级联效应发生时,就像福岛一样。我们在作者的类似通信网络模型CAINS(通信和信息网络求解器)的基础上讨论了一个数学模型,以协助对此类事件的备份和响应进行前瞻性和同步评估和规划。由于明确的原因,该模型放弃了蒙特卡罗模拟,具有分析性和统计性,避免了蒙特卡罗运行长度和混沌算法的问题,因此在规划大量失效模式时运行速度非常快。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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