An Advanced Computational Approach to System of Systems Analysis & Architecting Using Agent-Based Behavioral Model

This article describes the emerging roles of the systems engineering (SE) function in supporting enterprise management in information‐intensive organizations. “Enterprise Systems Engineering” (ESE) comprises three major roles or “levels” of systems engineering for successful and efficient development or procurement of large complex systems of systems (SoS). While the authors' experience focuses predominantly on government organizations acting as their own SoS integrators, the SoS ESE concept has generic applicability for any organization, public or private, seeking to attain competitive advantage through leveraging of information technology resources and systems. The processes and tools described here have been developed and successfully employed to facilitate government project management and investment decisions and control. [Carlock and Decker, 1998] This paper describes a formal three‐level SoS ESE process that, at the top level, organizes and maintains all of the details of the enterprise‐wide SoS architecture and strategic development plan in a flexible framework that accommodates the changes expected over a long SoS evolution. The information maintained in this framework allows the organization to know where it is going, how and when it is going to get there, the required capabilities and interfaces of each SoS component, and the impact of changes to system requirements, budgets, schedules, etc., on the overall SoS. The middle level processes allow the organization to perform trade studies among alternative solutions to implement required capabilities based on what is best for the enterprise‐wide SoS rather than just local considerations. The end result of the middle level processes is a selected and approved solution and its associated cost, schedule, benefits, and technical baselines. The third level processes implement the approved solutions in accordance with the approved baselines. © 2001 John Wiley & Sons, Inc. Syst Eng 4: 242–261, 2001

Modeling Resilience in System of Systems Architecture

SApHESIA: An agent-based model and a criticality-based heuristic for cooperatively coupling SoSs

The objective of this study is to investigate the value of an ecologically inspired architectural metric called the degree of system order in the system of systems (SoS) architecting process. Two highly desirable SoS attributes are the ability to withstand and recover from disruptions (resilience) and affordability. In practice, more resilient SoS architectures are less affordable, and it is essential to balance the trade-offs between the two attributes. Ecological research analyzing long-surviving ecosystems (nature’s resilient SoS) using the degree of system order metric has found a unique balance of efficient and redundant interactions in their architecture. This balance implies that highly efficient ecosystems tend to be inflexible and vulnerable to perturbations, while highly redundant ecosystems fail to utilize resources effectively for survival. Motivated by this unique architectural property of ecosystems, this study investigates the response to disruptions versus affordability trade space of a large number of feasible SoS architectures. Results indicate that the most favorable SoS architectures in this trade space share a specific range of values of degree of system order. This suggests that degree of system order can be a key metric in engineered SoS development. Evaluating the degree of system order does not require detailed simulations and can, therefore, guide the early-stage SoS design process toward more optimal SoS architectures.

An increasing number of software systems today are systems of systems (SoS) comprising decentralized and heterogeneous systems with operational and managerial independence. The evolution of SoS is the rule and not the exception in practice due to frequently changing requirements, technologies, and markets. However, providing adequate support for the evolution of SoS is rather challenging as their behavior often emerges at runtime and is difficult to predict. Hence, SoS must be monitored during simulation and operation to ensure compliance with its requirements. In this position paper, we present challenges for SoS evolution from the domain of industrial automation. We discuss existing approaches supporting SoS evolution and derive research issues. We outline requirements for SoS evolution support and present key capabilities of a flexible monitoring and evolution infrastructure. We conclude with a discussion of research perspectives.

Many software systems today have system-of systems (SoS) architectures comprising interrelated and heterogeneous systems, which are developed by multiple teams and companies. Such systems emerge gradually and it is hard to analyze or predict their behavior due to their scale and complexity. In particular, certain behavior only emerges at runtime due to complex interactions between the involved systems and their environment. Monitoring the behavior of SoS at runtime is thus essential during development and evolution. However, existing monitoring approaches are often limited to particular architectural styles or technologies and are thus hard to apply in SoS architectures. In this paper we first analyze the challenges for monitoring SoS based on an industrial SoS for the automation of metallurgical plants. We then propose a flexible framework for monitoring heterogeneous systems within a SoS. We demonstrate its feasibility by applying it to two systems of an industrial SoS. We also report results of an evaluation assessing the framework's performance and scalability.

Littoral combat is a critical component of naval warfare that has gained importance since the late 20th century due to a shift from matched blue-water combat to agile, asymmetric warfare near the coast. With the increased relevance of this type of naval combat, the ability of a networked naval warfare System-of-Systems (SoS) to operate effectively given the challenges of operations in the littoral zone becomes imperative. Resilience in an SoS in the face of a variety of threats is vital. These threats may take various forms: traditional warfare, cyber attacks, or communications breakdowns. The challenge for any SoS practitioner is identifying such architectures from a vast design space. We define an architecture as the constituent systems in the SoS and the connections among these systems. We seek to apply an agent-based modeling approach to create an architecture evaluation tool that leverages the modular nature of an SoS to quickly conduct trade studies and analyses to identify architectures that are resilient to a variety of threats. The simulation tool is built on the principles of the Open Systems Architecture put forth by the Department of Defense (DoD). We present an analysis that seeks to find a suitable SoS architecture configuration that maintains operability and performance during the loss of command authorities. This loss can be attributed to battle casualties or other forms of threats including communications failures. We demonstrate that distributing managerial control in the architecture improves, to some extent, SoS performance in terms of time to threat elimination. This single analysis demonstrates how SoS practitioners can use agent-based models to quickly construct and evaluate potential architectures against a variety of threats and disruptions, ultimately building resilient SoS architectures that perform to prescribed levels even in the face of network failures and attacks.

Most System-of-Systems (SoS) problems face a highly dynamic, volatile, and uncertain environment. How to cope with the unpredictable and volatile changes is one of the key challenges for SoS practitioners. Agility, as an informative feature indicating an SoS's capability of effecting, coping with and exploiting changes, has not received sufficient attention in the context of SoS. This paper proposes a dynamic reconfiguration method that aims to improve the SoS agility from the perspectives of responding quickly to failures and recovering some amount of the SoS capability. This paper employs approximate dynamic programming method to compute the dynamic reconfiguration decisions that allow failed or degraded systems to be removed and the function-capability allocation to be changed quickly. A naval warfare SoS example takes a preliminary step towards demonstrating the effectiveness of the dynamic reconfiguration method, along with flexible architecture, in achieving the SoS agility in terms of responsiveness and resilience.

A System-of-Systems (SoS) is a collection of distributed independent individual systems that interact with one another to achieve an SoS capability requirement that cannot be achieved by individual systems alone (in contrast, in a monolithic system, hardware or software components are integrated to form a single entity). Interdependency between SoS systems, while enabling new capability, also means that failures can cascade throughout the SoS, creating development delays or additional system failures. Here we develop a method based on Bayesian networks to evaluate the resilience of SoS design alternatives to failures during operations. We propose a conditional resilience metric that measures each constituent system's contribution to overall SoS resilience, and a resilience pattern that shows how SoS performance degrades as systems fail. SoS resilience is determined by both the SoS architecture and the constituent system reliability. In a simple example based on the Littoral Combat Ship SoS, we determine the two most critical systems using the conditional resilience metric. Adding a communications link between these two systems increases the resilience, resulting in higher expected performance and slower expected performance degradation as a result of system failure. The conditional resilience and resilience pattern can be used by designers and other decision makers to identify SoS architectures and systems that increase SoS resilience to failure.

Fuzzy Optimization of Acknowledged System of Systems Meta-architectures for Agent based Modeling of Development

Behavior models are key models in SoS (System of Systems) modeling and simulation in aircraft top-level demonstration. After architecture modeling to comprehensively describe the SoS, it is essential to transform the models for SoS mission simulation to achieve the virtual verification and validation in MBSE (Model-Based System Engineering). With the analysis of behavior modeling, two methods are introduced. Driving the entities in mission simulation system directly by the state machine models in SoS architecture after the interface definition is a proven method. Through the format conversion of the SoS architecture outputs, the operational/system state diagrams can be transformed to the corresponding organizations/systems behavior models in the mission simulation system. A sample case is provided to illustrate the continuous behavior modeling by format conversion in an air-to-surface attack mission. Compared to the traditional manual behavior modeling, the format conversion method leaves out the repeated behavior modeling and saves the corresponding working load.KeywordsBehavior modelingSystem of systemsModel transformation

Systems-of-Systems (SoS) is a set of independent software-intensive systems intended to support critical domains, such as emergency and crisis response systems and health. As such, SoS should exhibit robust behaviors and guarantee that the entire system is continuously available, not failing and leaving its users helpless. However, the SoS inherent dynamic architecture can potentially affect the availability of the functionalities being offered by the SoS. Hence, it is prominently important to anticipate, at design-time, the degree of availability an SoS can offer and provide mechanisms to reinforce the availability provided by it. Usually, software architects use static models to predict quality attributes in software architectures. However, these models are not well-succeeded to deal with the dynamic nature of the SoS architectures. The main contribution of this paper is thus presenting emerging results on the establishment of a simulation-based approach for empirically predicting the availability of SoS software architectures at design time. A Flood Monitoring SoS was used as the experimental platform. Preliminary results indicate that, specifically for the context of our study, the nature of availability in SoS architectures was not only influenced by the availability of the SoS constituents, but also by the SoS topology.

In the twenty-first century, mission success will require unprecedented interoperability among disparate constituent systems resulting in a System of Systems (SoS). Unlike traditional systems engineering where systems are created based on a set of user needs, a SoS is composed of multiple constituent systems, at various stages within their lifecycles (i.e. new start systems, systems in-development, legacy systems), to satisfy needed mission capabilities. System of Systems Engineering has been exploring appropriate methods and processes for almost two decades. This paper takes the discussion further by introducing a System of Systems Engineering and Integration (SOSE&I) methodology. SoSE&I is the planning, analyzing, and integrating of constituent systems into an SoS capability greater than the sum of those individual systems. The SoSE&I "Vee" process model is introduced and discusses how it is used to engineer the SoS throughout its lifecycle to increase systems integration and interoperability, and directly impact the operational success.

Systems-of-systems (SoS) are network-enabled synergistic collaborations between systems that are operationally and managerially independent, distributed, evolve dynamically and exhibit emergence. The design of dependable SoS requires model-based approaches that permit description of contracts between constituent systems at interfaces in a SoS architecture, including functionality and interaction behaviour, and that permit verification of global behaviours. We describe an approach to formal model-based SoS engineering using complementary notations for functional, interaction and architectural aspects. A case study in modelling information flow in an emergency response SoS demonstrates the viability of the proposed approach and highlights a need for common semantic foundations.

A System-of-Systems (SoS) is a complex, dynamic system whose Constituent Systems (CSs) are not known precisely at design time, and the environment in which they operate is uncertain. SoS behavior is unpredictable due to underlying architectural characteristics such as autonomy and independence. Although the stochastic composition of CSs is vital to achieving SoS missions, their unknown behaviors and impact on system properties are unavoidable. Moreover, unknown conditions and volatility have significant effects on crucial Quality Attributes (QAs) such as performance, reliability and security. Hence, the structure and behavior of a SoS must be modeled and validated quantitatively to foresee any potential impact on the properties critical for achieving the missions. Current modeling approaches lack the essential syntax and semantics required to model and verify SoS behaviors at design time and cannot offer alternative design choices for better design decisions. Therefore, the majority of existing techniques fail to provide qualitative and quantitative verification of SoS architecture models. Consequently, we have proposed an approach to model and verify Non-Deterministic (ND) SoS in advance by extending the current algebraic notations for the formal models as a hybrid stochastic formalism to specify and reason architectural elements with the required semantics. A formal stochastic model is developed using a hybrid approach for architectural descriptions of SoS with behavioral constraints. Through a model-driven approach, stochastic models are then translated into PRISM using formal verification rules. The effectiveness of the approach has been tested with an end-to-end case study design of an emergency response SoS for dealing with a fire situation. Architectural analysis is conducted on the stochastic model, using various qualitative and quantitative measures for SoS missions. Experimental results reveal critical aspects of SoS architecture model that facilitate better achievement of missions and QAs with improved design, using the proposed approach.