A network perspective on assessing system architectures: Foundations and challenges

Abstract

Organizations are increasingly faced with the challenge of architecting complex systems that must operate within a System of Systems context. While network science has offered usefully clear insights into product and system architectures, we seek to extend these approaches to evaluate enterprise system architectures. Here, we explore the application of graph‐theoretic methods to the analysis of two real‐world enterprise architectures (a military communications system and a search and rescue system) and to assess the relative importance of different architecture components. For both architectures, different topological measures of component significance identify differing network vertices as important. From this, we identify several significant challenges a system architect needs to be cognisant of when employing graph‐theoretic approaches to evaluate architectures; finding suitable abstractions of heterogeneous architectural elements and distinguishing between network‐structural properties and system‐functional properties. These challenges are summarized as five guiding principles for utilizing network science concepts for enterprise architecture evaluation.

A network perspective on assessing system architectures: Robustness to cascading failure

“…Several previous studies have analyzed network representations of system architectures, sometimes described as social network analysis , to support their evaluation 27–32 . This paper builds on previous work, 33–35 in seeking to explore the robustness and susceptibility to cascading failure of candidate system architectures from a network perspective, in order to assess the extent to which taking a network perspective on SoS architectures can assist architecture evaluation by helping determine whether one candidate architecture is more robust or resilient than another. Such an approach may address notions of dynamic complexity, such as: “what is the effect of the removal of some of these entities, whether in the form of a single failure or a cascade of failures?”…”

“…For further details about the use cases, the interested reader is directed to Ref. 33. The first use case is a Search and Rescue (SAR) NATO Architecture Framework (NAF)‐based architecture, developed by Thales in order to inform systems architecture training and help the development of NAF v4 36 .…”

“…Here, we follow previous studies 33–35 in representing each architecture as a network of vertices (representing Capabilities, Services, and Logical Nodes for the SAR use case, and representing Systems, Services, Functions, Components, Software. and Capability Configurations for the MComms use case) connected by edges (representing dependencies and relationships between vertices).…”

“…Due to the increasing scale and complexity of modern engineered systems, some have turned to a network representation of a system architecture to gain insights into various aspects such as, inter alia, architecture complexity, 31,45–47 modularity or community structure, 24,31,33 important architectural entities, 24,28,29,31,33,48–50 architecture topology, 24,28,29,31,33,49,50 etc, although there is little consensus on the most suitable definitions or measures of these properties (eg, what makes degree a more suitable measure of influence than closeness, betweenness or eigenvector centrality, or characteristic path length?) 33 . Within the engineering design community, a robust physical design can be interpreted as a design which minimizes interfaces between subsystems and maximizes interactions within subsystems, using a network representation of a physical system architecture (requirements decomposed into subsystems, decomposed into components) and clustering algorithms to determine the extent to which this design principle is applied 51 .…”

Assaying the importance of system complexity for the systems engineering community

“…Organizations are increasingly having to engineer complex systems to meet the needs of a connected world 1 . There are several challenges inherent in engineering novel complex systems; such systems are generally made up of a large number of diverse, interdependent subsystems and components, interconnected via nonlinear relationships, leading to difficulties in predicting overall system performance 2–8 . System complexity has been shown to negatively affect system delivery project outcomes 2 .…”