Planting parallel program simulation on the cloud

n/

On the Business Models of Cloud-based Modelling and Simulation for Decision Support

Simulation modelling is one of the techniques used for decision support in a wide range of domains and cloud computing is beginning to make some impact on simulation modelling by enabling ubiquitous, convenient and on-demand access to a variety of computing services. The cloud-based modelling and simulation (CBMS) literature has focused on how to develop CBMS tools using existing technologies. While this technical aspect is important, understanding the business aspect of CBMS is instrumental for its adoption by users and for ensuring the sustainability of the broader CBMS service supply chain. This paper presents a review of the business models adopted by vendors that provide Web or mobile applications for simulation modelling. An analysis of the offerings of these vendors provides some insights into how cloud services can be provided and used as part of CBMS business models. The study is conducted by reviewing the websites of simulation vendors. This study fills a gap in the literature on the business aspect of CBMS by providing insights into CBMS business model patterns. It highlights the importance of developing innovative business models that can help generate new market opportunities and revenue streams along the CBMS service supply chain. It also stresses the role of contracting in addressing the reported challenges and risks underpinning the provision and use of CBMS services

On the Business Models of Cloud-based Modelling and Simulation for Decision Support

Simulation modelling is one of the techniques used for decision support in a wide range of domains and cloud computing is beginning to make some impact on simulation modelling by enabling ubiquitous, convenient and on-demand access to a variety of computing services. The cloud-based modelling and simulation (CBMS) literature has focused on how to develop CBMS tools using existing technologies. While this technical aspect is important, understanding the business aspect of CBMS is instrumental for its adoption by users and for ensuring the sustainability of the broader CBMS service supply chain. This paper presents a review of the business models adopted by vendors that provide Web or mobile applications for simulation modelling. An analysis of the offerings of these vendors provides some insights into how cloud services can be provided and used as part of CBMS business models. The study is conducted by reviewing the websites of simulation vendors. This study fills a gap in the literature on the business aspect of CBMS by providing insights into CBMS business model patterns. It highlights the importance of developing innovative business models that can help generate new market opportunities and revenue streams along the CBMS service supply chain. It also stresses the role of contracting in addressing the reported challenges and risks underpinning the provision and use of CBMS services

High Speed Simulation Analytics

Simulation, especially Discrete-event simulation (DES) and Agent-based simulation (ABS), is widely used in industry to support decision making. It is used to create predictive models or Digital Twins of systems used to analyse what-if scenarios, perform sensitivity analytics on data and decisions and even to optimise the impact of decisions. Simulation-based Analytics, or just Simulation Analytics, therefore has a major role to play in Industry 4.0. However, a major issue in Simulation Analytics is speed. Extensive, continuous experimentation demanded by Industry 4.0 can take a significant time, especially if many replications are required. This is compounded by detailed models as these can take a long time to simulate. Distributed Simulation (DS) techniques use multiple computers to either speed up the simulation of a single model by splitting it across the computers and/or to speed up experimentation by running experiments across multiple computers in parallel. This chapter discusses how DS and Simulation Analytics, as well as concepts from contemporary e-Science, can be combined to contribute to the speed problem by creating a new approach called High Speed Simulation Analytics. We present a vision of High Speed Simulation Analytics to show how this might be integrated with the future of Industry 4.0

High Speed Simulation Analytics

Simulation, especially Discrete-event simulation (DES) and Agent-based simulation (ABS), is widely used in industry to support decision making. It is used to create predictive models or Digital Twins of systems used to analyse what-if scenarios, perform sensitivity analytics on data and decisions and even to optimise the impact of decisions. Simulation-based Analytics, or just Simulation Analytics, therefore has a major role to play in Industry 4.0. However, a major issue in Simulation Analytics is speed. Extensive, continuous experimentation demanded by Industry 4.0 can take a significant time, especially if many replications are required. This is compounded by detailed models as these can take a long time to simulate. Distributed Simulation (DS) techniques use multiple computers to either speed up the simulation of a single model by splitting it across the computers and/or to speed up experimentation by running experiments across multiple computers in parallel. This chapter discusses how DS and Simulation Analytics, as well as concepts from contemporary e-Science, can be combined to contribute to the speed problem by creating a new approach called High Speed Simulation Analytics. We present a vision of High Speed Simulation Analytics to show how this might be integrated with the future of Industry 4.0

New contributions of information technologies to develop distributed simulation

Se entiende por simulación al proceso por medio del cual serepresenta, reproduce o imita el comportamiento observable de un proceso osistema real a lo largo del tiempo y el espacio. La simulación distribuida tienela capacidad de acelerar la ejecución de un único modelo, vincular y reutilizarmúltiples modelos para simular modelos más grandes y acelerar la ejecuciónde etapas de experimentación. En este contexto, la construcción desimulaciones distribuidas ha mejorado en los últimos años gracias alsurgimiento de nuevas tecnologías de la información. En este artículo sedescriben los principios, modos de trabajo y enfoques de administración detiempo asociados a esta técnica junto con las herramientas de software que, enla actualidad, brindan soporte a su aplicación. Además, se presenta unarevisión bibliográfica que evidencia el crecimiento (y la importancia) de su usocomo técnica de estudio en diferentes dominios.Simulation is the process by which the observable behavior of a real process or system is represented, reproduced or imitated in time and space. Distributed simulation can be used for accelerate the execution of models, reuse models in larger models, and accelerate the execution of experiments. Given the emergence of new information technologies, the use of distributed simulation has grown. This paper describes the fundamentals, modes and time management approaches used in distributed simulations along with the software tools that improves its development. Also, a literature review is presented to show how this technique is applied in distinct domains.Fil: Sarli, Juan Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Blas, María Julia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Gonnet, Silvio Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin

Distributed Simulation: State-of-the-Art and Potential for Operational Research

In Operational Research conventional simulation practices typically focus on the conceptualization, development and use of a single model simulated on a single computer by a single analyst. Since the late 1970s the field of Distributed Simulation has led research into how to speed up simulation and how to compose large-scale simulations consisting of many reusable models running using distributed computers. There have been significant advances in the theories and technologies underpinning Distributed Simulation and there have been major successes in defence, computer systems design and smart urban environments. However, from an Operational Research perspective, Distributed Simulation has had little impact on mainstream research and practice. To argue the potential benefits of Distributed Simulation for Operational Research, this article gives an overview of Distributed Simulation approaches and technologies as well as discussing the state-of-the-art of Distributed Simulation applications. It will investigate the potential advantages of Distributed Simulation for Operational Research and present a possible sustainable future, based on experiences from e-Science, that will help Operational Research meet future challenges such as those emerging from Big Data Analytics, Cyber-physical systems, Industry 4.0, Digital Twins and Smart environments

Scheduling of a Cyber-Physical System Simulation

The work carried out in this Ph.D. thesis is part of a broader effort to automate industrial simulation systems. In the aeronautics industry, and more especially within Airbus, the historical application of simulation is pilot training. There are also more recent uses in the design of systems, as well as in the integration of these systems. These latter applications require a very high degree of representativeness, where historically the most important factor has been the pilot’s feeling. Systems are now divided into several subsystems that are designed, implemented and validated independently, in order to maintain their control despite the increase in their complexity, and the reduction in time-to-market. Airbus already has expertise in the simulation of these subsystems, as well as their integration into a simulation. This expertise is empirical; simulation specialists use the previous integrations schedulings and adapt it to a new integration. This is a process that can sometimes be time-consuming and can introduce errors. The current trends in the industry are towards flexible production methods, integration of logistics tools for tracking, use of simulation tools in production, as well as resources optimization. Products are increasingly iterations of older, improved products, and tests and simulations are increasingly integrated into their life cycles. Working empirically in an industry that requires flexibility is a constraint, and nowadays it is essential to facilitate the modification of simulations. The problem is, therefore, to set up methods and tools allowing a priori to generate representative simulation schedules. In order to solve this problem, we have developed a method to describe the elements of a simulation, as well as how this simulation can be executed, and functions to generate schedules. Subsequently, we implemented a tool to automate the scheduling search, based on heuristics. Finally, we tested and verified our method and tools in academic and industrial case studies