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.

Network Resiliency against Earthquakes

Valentini A.;Pace B.;
2019-01-01

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.
2019
978-1-7281-4698-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11564/716742
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