A method for examining the impact of interoperability on mission performance in a system-of-systems

Author(s):  
Burak Bagdatli ◽  
Kelly Griendling ◽  
David Kalpakchian ◽  
Elizabeth Jones ◽  
Sabrina Ussery ◽  
...  
Keyword(s):  
Author(s):  
Mateusz Iwo Dubaniowski ◽  
Hans Rudolf Heinimann

A system-of-systems (SoS) approach is often used for simulating disruptions to business and infrastructure system networks allowing for integration of several models into one simulation. However, the integration is frequently challenging as each system is designed individually with different characteristics, such as time granularity. Understanding the impact of time granularity on propagation of disruptions between businesses and infrastructure systems and finding the appropriate granularity for the SoS simulation remain as major challenges. To tackle these, we explore how time granularity, recovery time, and disruption size affect the propagation of disruptions between constituent systems of an SoS simulation. To address this issue, we developed a high level architecture (HLA) simulation of three networks and performed a series of simulation experiments. Our results revealed that time granularity and especially recovery time have huge impact on propagation of disruptions. Consequently, we developed a model for selecting an appropriate time granularity for an SoS simulation based on expected recovery time. Our simulation experiments show that time granularity should be less than 1.13 of expected recovery time. We identified some areas for future research centered around extending the experimental factors space.


2022 ◽  
Author(s):  
Sofia Schön ◽  
Ludvig Knöös Franzén ◽  
Carina Marcus ◽  
Kristian Amadori ◽  
Christopher Jouannet ◽  
...  

Author(s):  
Olav Skjelkvåle Ligaarden ◽  
Atle Refsdal ◽  
Ketil Stølen

Systems of systems are collections of systems interconnected through the exchange of services. Their often complex service dependencies and very dynamic nature make them hard to analyze and predict with respect to quality in general, and security in particular. In this chapter, the authors put forward a method for the capture and monitoring of impact of service dependencies on the security of provided services. The method is divided into four main steps focusing on documenting the system of systems and IT service dependencies, establishing the impact of service dependencies on risk to security of provided services, identifying measureable indicators for dynamic monitoring, and specifying their design and deployment, respectively. The authors illustrate the method in an example-driven fashion based on a case within power supply.


Author(s):  
Olav Skjelkvåle Ligaarden ◽  
Atle Refsdal ◽  
Ketil Stølen

Systems of systems are collections of systems interconnected through the exchange of services. Their often complex service dependencies and very dynamic nature make them hard to analyze and predict with respect to quality in general, and security in particular. In this chapter, the authors put forward a method for the capture and monitoring of impact of service dependencies on the security of provided services. The method is divided into four main steps focusing on documenting the system of systems and IT service dependencies, establishing the impact of service dependencies on risk to security of provided services, identifying measureable indicators for dynamic monitoring, and specifying their design and deployment, respectively. The authors illustrate the method in an example-driven fashion based on a case within power supply.


Author(s):  
Mateusz Iwo Dubaniowski ◽  
Hans Rudolf Heinimann

System-of-systems (SoS) approach is often used for simulating disruptions to business and infrastructure system networks allowing for integration of several models into one simulation. However, the integration is frequently challenging as each system is designed individually with different characteristics, such as time granularity. Understanding the impact of time granularity on propagation of disruptions between businesses and infrastructure systems and finding the appropriate granularity for the SoS simulation remain as major challenges. To tackle these, we explore how time granularity, recovery time, and disruption size affect the propagation of disruptions between constituent systems of an SoS simulation. To address this issue, we developed a High Level Architecture (HLA) simulation of 3 networks and performed a series of simulation experiments. Our results revealed that time granularity and especially recovery time have huge impact on propagation of disruptions. Consequently, we developed a model for selecting an appropriate time granularity for an SoS simulation based on expected recovery time. Our simulation experiments show that time granularity should be less than 1.13 of expected recovery time. We identified some areas for future research centered around extending the experimental factors space.


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