Systems-of-systems (SoS) are alliances of independent and interoperable software-intensive systems. SoS often support critical domains, being required to exhibit a reliable operation, specially because people’s safety relies on their services. In this direction, simulations enable the validation of different operational scenarios in a controlled environment, allowing a benchmarking of its response as well as revealing possible breaches that could lead to failures. However, simulations are traditionally manual, demanding a high level of human intervention, being costly and error-prone. A stimuli generator could aid in by continuously providing data to trigger a SoS simulation and maintaining its operation. We established a model-based approach termed Stimuli-SoS to support the creation of stimuli generators to be used in SoS simulations. Stimuli-SoS uses software architecture descriptions for automating the creation of such generators. Specifically, this approach transforms SoSADL, a formal architectural description language for SoS, into dynamic models expressed in DEVS, a simulation formalism. We carried out a case study in which Stimuli-SoS was used to automatically produce stimuli generators for a simulation of a flood monitoring SoS. We run simulations of a SoS architectural configuration with 69 constituent systems, i.e., 42 sensors, 9 crowdsourcing systems, and 18 drones. Stimuli generators were automatically generated for each type of constituent. These stimuli generators were capable of receiving the input data from the database and generating the expected stimuli for the constituents, allowing to simulate constituent systems interoperations into the flood monitoring SoS. Using Stimuli-SoS, we simulated 38 days of flood monitoring in little more than 6 h. Stimuli generators correctly forwarded data to the simulation, which was able to reproduce 29 flood alerts triggered by the SoS during a flooding event. In particular, Stimuli-SoS is almost 65 times more productive than a manual approach to producing data for the same type of simulation. Our approach succeeded in automatically deriving a functional stimuli generator that can reproduce environmental conditions for simulating a SoS. In particular, we presented new contributions regarding productivity and automation for the use of a model-based approach in SoS engineering.

https://link.springer.com/content/pdf/10.1186%2Fs13173-017-0062-y.pdf

Stimuli-SoS: a model-based approach to derive stimuli generators for simulations of systems-of-systems software architectures

Software has been increasingly embedded into systems (e.g., autonomous cars, traffic control systems, power distribution systems) to increase the precision of their functionalities, deliver automation, and make them smarter. Those systems have been combined and formed Systems-of-Systems (SoS) to realize futuristic software applications, such as smart cities. However, the complexity exhibited by them has also increased, claiming for techniques to support their engineering and quality. In this direction, modeling and simulation (M&S) have been established as valuable resources to deal with such complexity, potentially supporting an accurate prediction of SoS software correctness. The main contribution of this paper is reporting the results and advances achieved in the last five years of research on the use of M&S techniques to support Software Engineering of SoS. We report our experience, present learned lessons, and point for important challenges that must still be addressed.

Model-based engineering & simulation of software-intensive systems-of-systems: experience report and lessons learned

Complex system studies, especially in the aerospace context, where systems are too expensive and too critical to allow real experimentation, make extensive use of simulation A draw-back of the simulation approach is that, in general, aerospace systems studied are so large that their simulations are quite difficult to develop Indeed, such a simulation will generally involve simulation components coming from various sources and sharing information There, one may see two opposite constraints On the one hand, the simulation components are developed independently from each other (or potentially pre-exist) and hence need to manage on their own the data that they need On the other hand, overall data consistency has to be ensured in the simulation, especially for shared data An interesting way to solve this issue consists in structuring the data, ie defining a datamodel that will be used as a support to specify all operations relevant to data (access, storage,): one may see this approach as the application of model-driven methods to the central point of data in a simulation This paper presents GAMME, a home-made data-centered framework for the development of large-scale simulations, as well as uses of this framework in an ONERA simulation example

GAMME, a meta-model to unify data needs in simulation modeling (WIP)

Modeling and simulation play a major role in complex system engineering. In Systems of Systems (SoS) engineering, a special case of complex systems, they help to better understand and identify the side effects associated with the integration of autonomous constituent systems. Simulation is also a way of apprehending the emerging behaviors from this integration. The dynamic and evolving nature of the SoS environment has led us to rely on the most stable part to define them, namely their mission. In this paper we propose a simulation framework for SoS based on a conceptual model defining the mission. Mission is defined as a set of situations that require reactions. Situations are defined by rules on facts related to the SoS environment. Reactions are defined as orchestrations of services from constituent systems (subsystems) that must be triggered when a situation is identified.We present this simulation approach through an SoS case study on health assistance.

Situation/Reaction Paradigm for SoS Simulation

Entrepreneurship as a Multidisciplinary Project

Complex systems have become a particular area of interest in the past years and the study of such systems, seen as a whole, has yielded many and varied approaches. One of the difficulties frequently encountered is that such systems display emergence, i.e. their global effect or behavior is greater than the sum of the behaviors of their agents and depends strongly on the interactions between these agents. Simulation is a very interesting approach for this study, since it allows focusing on the dynamic study of the system, thus pointing out these behaviors and interactions. Moreover, it is particularly interesting for aerospace systems, which are nowadays clearly studied as complex systems, since these systems are generally poor candidates for real experimentation, due to many diverse reasons (e.g. cost for space systems, criticality in terms of human life for air transport systems, etc.). Hence, they must be studied quite thoroughly before their full design or realization is possible and simulation plays an important part in this. This paper presents methods that can be used to study complex systems by simulation, as well as some techniques that take into account their specific nature, in particular distributed simulation, a paradigm quite intensively used at Onera. It also deals with the difficulties that may be encountered in setting such large simulations up and also addresses the problem of data in such a large simulation, from the handling to the exploitation.

https://hal.archives-ouvertes.fr/hal-01184315/document

Simulation of Systems of Systems

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Simulation of Systems of Systems