Network recovery based on system crash early warning in a cascading failure model

Abstract This paper investigates the possibility of saving a network that is predicted to have a cascading failure that will eventually lead to a total collapse. We model cascading failures using the recently proposed KQ model. Then predict an impending total collapse by monitoring critical slowing...

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Autores principales:	Dong Zhou, Ahmed Elmokashfi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/60458ce572b54f22851fc651da4ba795
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spelling	oai:doaj.org-article:60458ce572b54f22851fc651da4ba7952021-12-02T16:08:02ZNetwork recovery based on system crash early warning in a cascading failure model10.1038/s41598-018-25591-62045-2322https://doaj.org/article/60458ce572b54f22851fc651da4ba7952018-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-25591-6https://doaj.org/toc/2045-2322Abstract This paper investigates the possibility of saving a network that is predicted to have a cascading failure that will eventually lead to a total collapse. We model cascading failures using the recently proposed KQ model. Then predict an impending total collapse by monitoring critical slowing down indicators and subsequently attempt to prevent the total collapse of the network by adding new nodes. To this end, we systematically evaluate five node addition rules, the effect of intervention delay and network degree heterogeneity. Surprisingly, unlike for random homogeneous networks, we find that a delayed intervention is preferred for saving scale free networks. We also find that for homogeneous networks, the best strategy is to wire newly added nodes to existing nodes in a uniformly random manner. For heterogeneous networks, however, a random selection of nodes based on their degree mostly outperforms a uniform random selection. These results provide new insights into restoring networks by adding nodes after observing early warnings of an impending complete breakdown.Dong ZhouAhmed ElmokashfiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-9 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dong Zhou Ahmed Elmokashfi Network recovery based on system crash early warning in a cascading failure model
description	Abstract This paper investigates the possibility of saving a network that is predicted to have a cascading failure that will eventually lead to a total collapse. We model cascading failures using the recently proposed KQ model. Then predict an impending total collapse by monitoring critical slowing down indicators and subsequently attempt to prevent the total collapse of the network by adding new nodes. To this end, we systematically evaluate five node addition rules, the effect of intervention delay and network degree heterogeneity. Surprisingly, unlike for random homogeneous networks, we find that a delayed intervention is preferred for saving scale free networks. We also find that for homogeneous networks, the best strategy is to wire newly added nodes to existing nodes in a uniformly random manner. For heterogeneous networks, however, a random selection of nodes based on their degree mostly outperforms a uniform random selection. These results provide new insights into restoring networks by adding nodes after observing early warnings of an impending complete breakdown.
format	article
author	Dong Zhou Ahmed Elmokashfi
author_facet	Dong Zhou Ahmed Elmokashfi
author_sort	Dong Zhou
title	Network recovery based on system crash early warning in a cascading failure model
title_short	Network recovery based on system crash early warning in a cascading failure model
title_full	Network recovery based on system crash early warning in a cascading failure model
title_fullStr	Network recovery based on system crash early warning in a cascading failure model
title_full_unstemmed	Network recovery based on system crash early warning in a cascading failure model
title_sort	network recovery based on system crash early warning in a cascading failure model
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/60458ce572b54f22851fc651da4ba795
work_keys_str_mv	AT dongzhou networkrecoverybasedonsystemcrashearlywarninginacascadingfailuremodel AT ahmedelmokashfi networkrecoverybasedonsystemcrashearlywarninginacascadingfailuremodel
_version_	1718384658372100096

Network recovery based on system crash early warning in a cascading failure model

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