Risk indicators of cascading failures in interconnected network structures
- 作者: Tyrsin A.N.1, Kashcheev S.E.2
-
隶属关系:
- Science and Engineering Center “Reliability and Resource of Large Systems and Machines”, Ural Branch of RAS, Yekaterinburg, Institute of Economics
- South-Ural State University
- 期: 编号 102 (2023)
- 页面: 76-98
- 栏目: Networking in control sciences
- URL: https://medbiosci.ru/1819-2440/article/view/363793
- DOI: https://doi.org/10.25728/ubs.2023.102.5
- ID: 363793
如何引用文章
全文:
详细
The behavior of real systems is often stochastic, and the connections between their elements can be adequately described as correlations. In recent years, there have been trends of increasing and complicating modern networks with the growth of their dependence on each other. We observe how several networks are combined into one interdependent network structure. This leads to an increase in the risks that the failure of nodes in one network may lead to the failure of dependent nodes in other networks. As a result of such failures, catastrophic cascade failures can occur in such interconnected network structures. Given the scale of such structures, which are often critical infrastructures, this problem becomes very relevant. The article introduces a scalar measure of the relationship between several arbitrarily distributed continuous random vectors. It allows you to assess the closeness of the relationship between different subsystems (networks) in network structures. Applied to Gaussian model network structures, the influence of the closeness of the relationship between subsystems on the risk of cascading failures has been studied. The probability of such failures was used as the risk value. As an indicator of the risk of cascading failures in the network structure, it is proposed to use the coefficient of correlation between its subsystems. And to reduce the risk of cascading failures in the network structure, it is necessary to reduce the tightness of correlation between the most interconnected elements of subsystems.
作者简介
Alexander Tyrsin
Science and Engineering Center “Reliability and Resource of Large Systems and Machines”, Ural Branch of RAS, Yekaterinburg, Institute of Economics
Email: at2001@yandex.ru
Ural Branch of RAS
Stanislav Kashcheev
South-Ural State University
Email: kashcheevs@susu.ru
Chelyabinsk
参考
1. АЛЕКСЕЕВ В.В., СОЛОЖЕНЦЕВ Е.Д. Логико-вероятностный подход к управлению риском и эффек-тивностью в структурно-сложных системах // Инфор-мационно-управляющие системы. – 2009. – №6(43). – С. 67–71. 2. АШНИНА Ю.А. БОРИСОВ А.В., БОРИСОВА Н.И. Раз-витие инфраструктуры современного города: социаль-ные и экономические аспекты // NovaInfo. – 2015. – №39. – С. 177–183. 3. БУЛАТОВ В.В. Введение в математические методы моделирования сложных систем. – М.: ОнтоПринт, 2018. – 338 с. 4. МИХАЙЛОВ Г.А., ВОЙТИШЕК А.В. Численное стати-стическое моделирование. Методы Монте-Карло. – М.: Издательский центр «Академия», 2006. – 368 с. 5. НОВИКОВ Д.А. Сетевые структуры и организационные системы. – М.: ИПУ РАН, 2003. – 102 с. 6. Современный философский словарь: 2-е изд. / Под общ. ред. В.Е. Кемерова. – М.: ПАНПРИНТ, 1998. – 1064 с. 7. ТЫРСИН А.Н. Мера совместной корреляционной зави-симости многомерных случайных величин // Заводская лаборатория. Диагностика материалов. – 2014. – Т. 80, №1. – С. 76–80. 8. ТЫРСИН А.Н. Скалярная мера взаимосвязи между не-сколькими случайными векторами // Заводская лабора-тория. Диагностика материалов. – 2022. – Т. 88, №3. – С. 73–80. 9. ФИХТЕНГОЛЬЦ Г.М. Курс дифференциального и инте-грального исчисления. В 3 т. Т.1. – 8-е изд. – М.: ФИЗ-МАТЛИТ, 2003. – 680 с. 10. ХАРДЛЕ В. Прикладная непараметрическая регрессия: Пер. с англ. – М.: Мир, 1993. – 349 с. 11. BEHRENSDORF J., BROGGI M., BEER M. Reliability Analysis of Networks Interconnected with Copulas // ASCE-ASME. Journal of Risk and Uncertainty in Engineering Sys-tems, Part B Mechanical Engineering. – 2019. – Vol.5. – P. 041006-9. 12. BULDYREV S.V., PARSHANI R., PAUL G., STAN-LEY H.E., HAVLIN SH. Catastrophic cascade of failures in interdependent networks // Nature. – 2010. – Vol. 464. – P. 1025–1028. 13. CHERUBINI U., LUCIANO E., VECCHIATO W. Copula Methods in Finance. – Wiley, Chichester, UK, 2004. 14. ENGLE R.F. Anticipating Correlations. A New Paradigm for Risk Management. – Princeton University Press, 2009. 15. GOODWIN B.K., HUNGERFORD A. Copula-Based Models of Systemic Risk in us Agriculture: Implications for Crop In-surance and Reinsurance Contracts // American Journal of Agricultural Economics. – 2014. – Vol. 97, No 3. – P. 879–896. 16. GORBAN A.N., TYUKINA T.A., POKIDYSHEVA L.I., SMIRNOVA E.V. Dynamic and thermodynamic models of adaptation // Physics of Life Reviews. – 2021. – Vol. 37. – P.17–64. 17. JOE H. Dependence Modeling with Copulas. – Chapman and Hall/CRC, New York, 2014. 18. LAPRIE J.C., KANOUN K., KANICHE M. Modeling inter-dependencies between the electricity and information infra-structures // SAFECOMP-2007. – 2007. – Vol. 4680. – P. 54–67. 19. PENA D., RODRIGUEZ J. Descriptive Measures of Multi-variate Scatter and Linear Dependence // Journal of Multi-variate Analysis. – 2003. – Vol. 85, No 2. – P. 361–374. 20. RAM M., SINGH S.B. Analysis of Reliability Characteristics of a Complex Engineering System under Copula // Journal of Reliability and Statistical Studies. – 2009. – Vol. 2, No 1. – P. 91–102. 21. RINALDI S., PEERENBOOM J., KELLY T. Identifying, un-derstanding, and analyzing critical infrastructure interde-pendencies // IEEE Control Systems Magazine. – 2001. – Vol. 21. – P. 11–25.
补充文件
