Abstract
Guaranteeing a high availability of network services is a crucial part of network management. In this study, we show how to compute the availability of network services under earthquakes, by using empirical data. We take a multi-disciplinary approach and create an earthquake model based on seismological research and historical data. We then show how to integrate this empirical disaster model into existing network resiliency models to obtain the vulnerability and availability of a network under earthquakes. While previous studies have applied their models to ground shaking hazard models or earthquake scenarios, we compute (1) earthquake activity rates and (2) a relation between magnitude and disaster area, and use both as input data for our modeling. This approach is more in line with existing network resiliency models: it provides better information on the correlation between link failures than ground shaking hazard models and a more comprehensive view than a fixed set of scenarios.
Original language | American English |
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Title of host publication | Proceedings of 2019 11th International Workshop on Resilient Networks Design and Modeling, RNDM 2019 |
Editors | Georgios Ellinas, Jacek Rak, Roza Goscien |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781728146980 |
DOIs | |
State | Published - Oct 2019 |
Event | 11th International Workshop on Resilient Networks Design and Modeling, RNDM 2019 - Nicosia, Cyprus Duration: 14 Oct 2019 → 16 Oct 2019 |
Publication series
Name | Proceedings of 2019 11th International Workshop on Resilient Networks Design and Modeling, RNDM 2019 |
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Conference
Conference | 11th International Workshop on Resilient Networks Design and Modeling, RNDM 2019 |
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Country/Territory | Cyprus |
City | Nicosia |
Period | 14/10/19 → 16/10/19 |
Bibliographical note
Funding Information:Part of this work has been supported by COST Action CA15127 (RE-CODIS), the Hungarian Scientific Research Fund (grant No. OTKA K124171 and K128062), by the BME-Artificial Intelligence FIKP grant of EMMI (BME FIKP-MI/SC), and the HUJI Cyber Security Center in conjunction with the Israel National Cyber Directorate in the Prime Minister’s Office.
Funding Information:
Part of this work has been supported by COST Action CA15127 (RECODIS), the Hungarian Scientific Research Fund (grant No. OTKA K124171 and K128062), by the BME-Artificial Intelligence FIKP grant of EMMI (BME FIKP-MI/SC), and the HUJI Cyber Security Center in conjunction with the Israel National Cyber Directorate in the Prime Minister's Office.
Publisher Copyright:
© 2019 IEEE.