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Operational resilience: concepts, design and analysis
Building resilience into today’s complex infrastructures is critical to the daily functioning of society and its ability to withstand and recover from natural disasters, epidemics, and cyber-threats. This study proposes quantitative measures that capture and implement the definition of engineering r...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4726063/ https://www.ncbi.nlm.nih.gov/pubmed/26782180 http://dx.doi.org/10.1038/srep19540 |
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author | Ganin, Alexander A. Massaro, Emanuele Gutfraind, Alexander Steen, Nicolas Keisler, Jeffrey M. Kott, Alexander Mangoubi, Rami Linkov, Igor |
author_facet | Ganin, Alexander A. Massaro, Emanuele Gutfraind, Alexander Steen, Nicolas Keisler, Jeffrey M. Kott, Alexander Mangoubi, Rami Linkov, Igor |
author_sort | Ganin, Alexander A. |
collection | PubMed |
description | Building resilience into today’s complex infrastructures is critical to the daily functioning of society and its ability to withstand and recover from natural disasters, epidemics, and cyber-threats. This study proposes quantitative measures that capture and implement the definition of engineering resilience advanced by the National Academy of Sciences. The approach is applicable across physical, information, and social domains. It evaluates the critical functionality, defined as a performance function of time set by the stakeholders. Critical functionality is a source of valuable information, such as the integrated system resilience over a time interval, and its robustness. The paper demonstrates the formulation on two classes of models: 1) multi-level directed acyclic graphs, and 2) interdependent coupled networks. For both models synthetic case studies are used to explore trends. For the first class, the approach is also applied to the Linux operating system. Results indicate that desired resilience and robustness levels are achievable by trading off different design parameters, such as redundancy, node recovery time, and backup supply available. The nonlinear relationship between network parameters and resilience levels confirms the utility of the proposed approach, which is of benefit to analysts and designers of complex systems and networks. |
format | Online Article Text |
id | pubmed-4726063 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-47260632016-01-27 Operational resilience: concepts, design and analysis Ganin, Alexander A. Massaro, Emanuele Gutfraind, Alexander Steen, Nicolas Keisler, Jeffrey M. Kott, Alexander Mangoubi, Rami Linkov, Igor Sci Rep Article Building resilience into today’s complex infrastructures is critical to the daily functioning of society and its ability to withstand and recover from natural disasters, epidemics, and cyber-threats. This study proposes quantitative measures that capture and implement the definition of engineering resilience advanced by the National Academy of Sciences. The approach is applicable across physical, information, and social domains. It evaluates the critical functionality, defined as a performance function of time set by the stakeholders. Critical functionality is a source of valuable information, such as the integrated system resilience over a time interval, and its robustness. The paper demonstrates the formulation on two classes of models: 1) multi-level directed acyclic graphs, and 2) interdependent coupled networks. For both models synthetic case studies are used to explore trends. For the first class, the approach is also applied to the Linux operating system. Results indicate that desired resilience and robustness levels are achievable by trading off different design parameters, such as redundancy, node recovery time, and backup supply available. The nonlinear relationship between network parameters and resilience levels confirms the utility of the proposed approach, which is of benefit to analysts and designers of complex systems and networks. Nature Publishing Group 2016-01-19 /pmc/articles/PMC4726063/ /pubmed/26782180 http://dx.doi.org/10.1038/srep19540 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Ganin, Alexander A. Massaro, Emanuele Gutfraind, Alexander Steen, Nicolas Keisler, Jeffrey M. Kott, Alexander Mangoubi, Rami Linkov, Igor Operational resilience: concepts, design and analysis |
title | Operational resilience: concepts, design and analysis |
title_full | Operational resilience: concepts, design and analysis |
title_fullStr | Operational resilience: concepts, design and analysis |
title_full_unstemmed | Operational resilience: concepts, design and analysis |
title_short | Operational resilience: concepts, design and analysis |
title_sort | operational resilience: concepts, design and analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4726063/ https://www.ncbi.nlm.nih.gov/pubmed/26782180 http://dx.doi.org/10.1038/srep19540 |
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