Decentralized Approach for Efficient Simulation of Devs Models

Franceschini, Romain; Bisgambiglia, Paul-Antoine

doi:10.1007/978-3-662-44733-8_42

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…Someone may believe that adevs loses on speed each time, for the simulation of more complex composite gates. For that we can use new architectures enhancing simulation process like those proposed by (Kim et al, 2000) and (Franceschini and Bisgambiglia, 2014) to reduce intermediary processes, and so the messages will cross short paths. What this suggests that adevs and DEVS simulator in C++ may provide interesting simulation execution times of logic gate close to Verolig.…”

Section: Performance Comparisonmentioning

confidence: 99%

Modeling and Simulation of Logic Gates using DEVS

Hamri

Driouche²

2015

Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

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Discrete event simulation becomes popular more and more and was applied successfully in many fields: medicine, robotics, etc. One of this field is digital circuits for which boolean logics is the basis of computation by designing logic gates. However such a paradigm does not consider the time basis. Consequently, the boolean logic paradigm can not design and simulate delays of circuits and stamped explicitly computations. In this paper, we propose to combine the boolean logic paradigm and Discrete EVent system Specification (DEVS) formalism for modeling and simulation logic gates. Using this approach, we are able to design complex network of logic gates by reusing and coupling basic ones and to analyze behavior through time. 2 BACKGROUND DEVS is one of the popular discrete event formalisms proposed in 70´s by Zeigler (Zeigler, 1976). The DEVS M&S framework separates clearly modeling 212 Hamri M. and Driouche N.. Modeling and Simulation of Logic Gates using DEVS.

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Section: Performance Comparisonmentioning

confidence: 99%

Modeling and Simulation of Logic Gates using DEVS

Hamri

Driouche²

2015

Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

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“…Some authors have used it as a metric to evaluate their DEVS implementations [39,108]. Others used it for measuring the impact of original proposals for performance improvement [109]. Several works have compared some of the most relevant DEVS-based simulators of the state of the art using this benchmark [110,111].…”

Section: Devs Simulation Performancementioning

confidence: 99%

Integrative Modeling, Simulation, and Optimization Techniques for Efficient Data-Intensive Applications in Edge Computing Infrastructures

Cardenas Rodriguez

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The fusion of technologies that characterizes the Fourth Industrial Revolution (4IR) is blurring the boundaries between the physical and digital worlds. New solutions are increasingly complex and involve expertise in multiple domains. Thus, they are no longer considered stand-alone but as a System of Systems (SoS) comprising devices, users, and other supporting systems. The conception, design, and deployment of today's SoS require an exhaustive analysis of the scenario for succeeding. Modeling and Simulation-Based Systems Engineering (MSBSE) techniques are a variant of Systems Engineering (SE) that apply advanced Modeling and Simulation (M&S) procedures to ensure a logical, robust, and reliable incremental design, allowing us to assess the technical risks of the system while decreasing the operational and capital costs of the solution under development. However, these methods still present deficiencies that require further research to cover more use cases and achieve greater adoption in industry and academia. Using a monolithic computational model for describing the entire system usually incurs high simulation times. Usually, simulating a single scenario of a complex system takes considerably longer than the simulated time interval. Therefore, an in-depth exploration of all the possible hypotheses becomes unbearable. For that reason, we need to establish a new Modeling, Simulation, and Optimization (M&S&O) workflow for assisting in the conception, design, implementation, and operation phases of modern complex systems that tackle the problems presented above without compromising the robustness of the solution.

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