Abstract
The complexity of modern systems of systems (SoS) requires the ability to quickly and effectively evaluate robustness in architecture alternatives. This article presents a framework for rapid, quantitative comparisons of robustness in SoS architectures that leverages complex network methods for assessing robustness and design of experiments (DoE) techniques for validating their use in the scenario of interest. We consider both single-layer and multilayer network representations of SoS and focus on algebraic connectivity, inverse average path length, and largest connected component size as measures of robustness. Two case studies are used to illustrate our framework and assess its utility: a command, control, communications, computer, intelligence, surveillance, and reconnaissance (C4ISR) simulation and a multilayer message-passing network simulation. We find that most of the considered network metrics capture expected robustness trends, though their ability to capture these trends is often affected by the scenario of interest. These results demonstrate the potential value of complex network methods for lightweight analysis of robustness in SoS architecture alternatives, when appropriately supported by DoE methods for understanding their limitations.
Highlights
M ODERN systems are becoming increasingly networked to form systems of systems (SoS), providing novel capabilities across many domains
Our framework is composed of following three steps: 1) we model the SoS of interest as a single-layer or multilayer network. 2) we implement a set of complex network metrics for lightweight measurements of robustness in alternative architectures for this SoS. 3) we use design of experiments (DoE) techniques to efficiently validate and assess the utility of these metrics for the domain of interest
We present a framework for analyzing SoS robustness that uses complex network methods able to capture single-layer and multilayer architectures, supported by a DoE approach for validating the use of those methods for a scenario of interest
Summary
M ODERN systems are becoming increasingly networked to form systems of systems (SoS), providing novel capabilities across many domains. Others have investigated techniques for generating feasible SoS alternative architectures [15], optimizing architectures [16], [17], approximating SoS performance with surrogate models [18], and identifying important “-ilities” based on subjective input from decision-makers [19]. While these efforts support tradespace analysis, they do not provide direct means for assessing SoS robustness within those trade studies
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