Mercury: A modeling, simulation, and optimization framework for data stream-oriented IoT applications

Cárdenas, Román; Arroba, Patricia; Blanco, Roberto; Malagón, Pedro; Risco‐Martín, José L.; Moya, José Manuel Huidobro

doi:10.1016/j.simpat.2019.102037

Cited by 12 publications

(13 citation statements)

References 10 publications

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“…Table 1 shows a brief comparison of the edge computing simulators previously mentioned. Mercury [14] corresponds to the M&S&O framework presented in this paper. As our research focuses on edge computing infrastructures, Mercury had not previously considered cloud computing facilities.…”

Section: Edge Computing Infrastructuresmentioning

confidence: 99%

“…However, the discrete-time nature of Mosaik leads to a substantial performance penalty when time accuracy is required. Table 2 compares these simulators with the new version of Mercury [14] presented in this paper. Note that the smart grid model implemented by Mercury is focused on the customer side and does not implement aspects that belong to other domains.…”

Section: Smart Gridsmentioning

confidence: 99%

“…The hierarchical and modular approach of the DEVS formalism eases the integration of models with various degrees of detail to capture the interrelationships between different complex systems. Mercury, our M&S&O framework [14], implements the presented model as an extension. This new version enables the optimization of edge computing facilities connected to smart grids, reducing the carbon footprint of IoT services that benefit from this infrastructure.…”

Section: Edge Computing and Smart Gridsmentioning

confidence: 99%

“…-We extend our edge computing model presented in previous publications [14,15] to integrate cooling infrastructures and support dynamic and more advanced resource management policies. -We also expand the model to support a decentralized load balancing protocol to map new loads to one of the EDCs comprising the edge computing federation.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and simulation of smart grid-aware edge computing federations

et al. 2022

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Compute-intensive Internet of Things (IoTs) applications have led to the edge computing paradigm. Edge computing decentralizes the IT infrastructure in multiple edge data centers (EDCs) across the access networks to reduce latency and network congestion. Edge computing can benefit significantly from different aspects of smart grids to achieve lower energy consumption and greater resilience to electricity price fluctuations. This paper presents a modeling, simulation, and optimization (M&S&O) framework for analyzing and dimensioning smart grid-aware edge computing federations. This tool integrates aspects of a consumer-centric smart grid model to the resource management policies of the EDCs. To illustrate the benefits of this tool, we show a realistic case study for optimizing the energy consumption and operational expenses of an edge computing federation that provides service to a driver assistance IoT application. Results show that this approach can reduce the daily energy consumption by 20.3% and the electricity budget by 30.3%.

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Section: Edge Computing Infrastructuresmentioning

confidence: 99%

Section: Smart Gridsmentioning

confidence: 99%

Section: Edge Computing and Smart Gridsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling and simulation of smart grid-aware edge computing federations

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“…Meanwhile, it is more convenient than the process model in the previous MDO optimization strategy to process coupled information. However, there are relatively few researches on integrating the system model generated by the system design stage and the optimization model generated in the subsequent multidisciplinary optimization [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Multidisciplinary Modeling and Optimization Method of Remote Sensing Satellite Parameters Based on SysML-CEA

Chu¹,

Yin²,

Shi³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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To enhance the efficiency of system modeling and optimization in the conceptual design stage of satellite parameters, a system modeling and optimization method based on System Modeling Language and Co-evolutionary Algorithm is proposed. At first, the objectives of satellite mission and optimization problems are clarified, and a design matrix of discipline structure is constructed to process the coupling relationship of design variables and constraints of the orbit, payload, power and quality disciplines. In order to solve the problem of increasing nonlinearity and coupling between these disciplines while using a standard collaborative optimization algorithm, an improved genetic algorithm is proposed and applied to system-level and discipline-level models. Finally, the CO model of satellite parameters is solved through the collaborative simulation of Cameo Systems Modeler (CSM) and MATLAB. The result obtained shows that the method proposed in this paper for the conceptual design phase of satellite parameters is efficient and feasible. It can shorten the project cycle effectively and additionally provide a reference for the optimal design of other complex projects.

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xDEVS: A toolkit for interoperable modeling and simulation of formal discrete event systems

et al. 2022

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Employing Modeling and Simulation (M&S) extensively to analyze and develop complex systems is the norm today. The use of robust M&S formalisms and rigorous methodologies is essential to deal with complexity. Among them, the Discrete Event System Specification (DEVS) provides a solid framework for modeling structural, behavior and information aspects of any complex system.This gives several advantages to analyze and design complex systems: completeness, verifiability, extensibility, and maintainability. DEVS formalism has been implemented in many programming languages and executable on multiple platforms. In this paper, we describe the features of an M&S framework called xDEVS that builds upon the prevalent DEVS Application Programming Interface (API) for both modeling and simulation layers, promoting interoperability between the existing platform-specific (C++, Java, Python) DEVS implementations. Additionally, the framework can simulate the same model using sequential, parallel, or distributed architectures. The M&S engine has been reinforced with several strategies to improve performance, as well as tools to perform model analysis and verification. Finally, xDEVS also facilitates systems engineers to apply the vision of model-based systems engineering (MBSE), model-driven engineering (MDE), and model-driven systems engineering (MDSE) paradigms.We highlight the features of the proposed xDEVS framework with multiple examples and case studies illustrating the rigor and diversity of application domains it can support.

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Mercury: A modeling, simulation, and optimization framework for data stream-oriented IoT applications

Cited by 12 publications

References 10 publications

Modeling and simulation of smart grid-aware edge computing federations

Modeling and simulation of smart grid-aware edge computing federations

Multidisciplinary Modeling and Optimization Method of Remote Sensing Satellite Parameters Based on SysML-CEA

xDEVS: A toolkit for interoperable modeling and simulation of formal discrete event systems

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