Multilevel Agent-Based Modeling of System of Systems

A distributed system's functionality must continuously evolve, especially when environmental context changes. Such required evolution imposes unbearable complexity on system development. An alternative is to make systems able to self-adapt by opportunistically composing at runtime to generate systems of systems (SoSs) that offer value-added functionality. The success of such an approach calls for abstracting the heterogeneity of systems and enabling the programmatic construction of SoSs with minimal developer intervention. We propose a general ontology-based approach to describe distributed systems, seeking to achieve abstraction and enable runtime reasoning between systems. We also propose an architecture for systems that utilize such ontologies to enable systems to discover and 'understand' each other, and potentially compose, all at runtime. We detail features of the ontology and the architecture through two contrasting case studies. We also quantitatively evaluate the scalability and validity of our approach through experiments and simulations. Our approach enables system developers to focus on high-level SoS composition without being tied down with the specific deployment-specific implementation details. CCS CONCEPTS• Computer systems organization → Architectures; • Computer systems organization Self-organizing autonomic computing;

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“…Agent-based ontological approaches have been proposed, e.g., [2,3,13,33]. However, these works are less systematic than our holon ontology.…”

Section: Problem Space and Related Workmentioning

confidence: 99%

An ontological architecture for principled and automated system of systems composition

Elhabbash

Nundloll

Elkhatib

et al. 2020

Proceedings of the IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

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“…This includes sophisticated interactive component systems that evolve in a dynamic environment. Based on mainstream modelling of the System of Systems; J.-B et.al., In 2015 [7] provides a general description of the complexity and control of the SOS. The researchers built a Multilevel Simulation agent-based model that organizes SoS.…”

Section: System Of Systems (Sos)mentioning

confidence: 99%

Enhancement Belief Function with Ontology to Solve the Conflict for the System of Systems

Younes¹,

Ahmed²,

Elsayed³

2019

IJIES

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System of Systems (SoS) is a huge system consisting of small stand-alone systems to offer unique abilities. In such complex construction, managing achievement of SoS objectives along with those of its Component Systems (CS) is a challenge. Whereas the integration of CS may cause an extended impact issue called conflict. In this research, the principles of basic belief function were used to measure conflict in the SoS. This is intended to detect conflicts between CS in an attempt to resolve it. Moreover, we have based on the ontology in the SoS integration. In addition to the identification of relationships between the CS semantically. We have proposed a method to resolve conflicts called Ontology Belief Function System of Systems (OBFSoS). As a homogenous SoS, we apply the OBFSoS on Real-lime Strategy (RTS) game. We depend on the use of a learning agent. It reduces conflicts and saves time due to attempts. For execution, we got the result of 10 attempts with about 30 systems (players) in the SoS. It giving a good decision in a short time with an accuracy of approximately 87%.

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“…Soyez et al propose an agent-based tool to support modeling of static and dynamic aspects of SoS [70]. Their formalism is based on the multi-level agent-based model IRM4MLS, which allows the representation of multiple entities that can interoperate at different levels, i.e., a constituent can be itself an SoS, hence supporting different levels of granularity [72].…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2017

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Background: 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. Methods: 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. Results: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. Conclusions: 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.

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Multilevel Agent-Based Modeling of System of Systems

Cited by 30 publications

References 26 publications

An ontological architecture for principled and automated system of systems composition

An ontological architecture for principled and automated system of systems composition

Enhancement Belief Function with Ontology to Solve the Conflict for the System of Systems

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

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